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111 483 



WHAT COMPUTERS CAN'T DO 



A CRITIQUE 



NEW YORK, EVANSTON, SAN FRANCISCO, LONDON 




1817 



WHAT COMPUTERS 

CAN'T DO 



OF ARTIFICIAL REASON 



By Hubert L. Dreyfus 



HARPER & ROW, PUBLISHERS 



The following materials are used with permission: 

The illustration on page 7 from Computers and Thought by Feigenbaum and Feldman. 
Copyright 1963 by Mcgraw-Hill, Inc. Used with permission of McGraw-Hill Book Com- 
pany. 

Quotations in Chapter 2 from Ross Quillian reprinted from "Semantic Memory" in Seman- 
tic Information Processing edited by Marvin Minsky by permission of The M.I.T. Press, 
Cambridge, Massachusetts. Copyright 1968 by the Massachusetts Institute of Technology. 

Quotations in Chapter 2 from Thomas G. Evans reprinted from "A Program for the 
Solution of a Class of Geometric- Analogy Intelligence Test Questions" in Semantic Infor- 
mation Processing edited by Marvin Minsky by permission of The M.I.T. Press, Cam- 
bridge, Massachusetts. Copyright 1968 by the Massachusetts Institute of Technology. 

Quotations in Chapter 7 from Anthony Oettinger reprinted from, and by permission of, 
American Documentation, July 1968, vol. 19, no. 3, pp. 295-298. Copyright 1968 by 
American Society for Information Science, 1 140 Connecticut Avenue, N.W., Washington, 
D.C 20036. 



WHAT COMPUTERS CAN'T DO: A CRITIQUE OF ARTIFICIAL REASON. Copyright 7972 by 

Hubert L. Dreyfus. All rights reserved. Printed in the United States of America. No part of 
this book may be used or reproduced in any manner whatsoever without written permission 
except in the case of brief quotations embodied in critical articles and reviews. For informa- 
tion address Harper & Row, Publishers, Inc., 49 East 33rd Street, New York, N. Y. 10016. 
Published simultaneously in Canada by Fitzhenry & Whiteside Limited, Toronto. 

FIRST EDITION 

STANDARD BOOK NUMBER: 06-011082-1 

LIBRARY OF CONGRESS CATALOG CARD NUMBER: 67-22524 



The difference between the mathematical mind {esprit de geome- 
trie) and the perceptive mind {esprit de finesse): the reason that math- 
ematicians are not perceptive is that they do not see what is before them, 
and that, accustomed to the exact and plain principles of mathematics, 
and not reasoning till they have well inspected and arranged their princi- 
ples, they are lost in matters of perception where the principles do not 
allow for such arrangement. . . . These principles are so fine and so 
numerous that a very delicate and very clear sense is needed to perceive 
them, and to judge rightly and justly when they are perceived, without 
for the most part being able to demonstrate them in order as in math- 
ematics; because the principles are not known to us in the same way, and 
because it would be an endless matter to undertake it. We must see the 
matter at once, at one glance, and not by a process of reasoning, at least 
to a certain degree. . . . Mathematicians wish to treat matters of percep- 
tion mathematically, and make themselves ridiculous . . . the mind . . . 
does it tacitly, naturally, and without technical rules. 

PASCAL, Pensees 



Contents 



Preface xi 

Acknowledgments xiv 

Introduction xv 

Part I. Ten Years of Research in Artificial 
Intelligence (1957-1967) 

1. Phase I (1957-1962) Cognitive Simulation 3 

i. Analysis of Work in Language Translation, Problem 

Solving, and Pattern Recognition 
II. The Underlying Significance of Failure to Achieve 
Predicted Results 

2. Phase II (1962-1967) Semantic Information Processing 42 

I. Analysis of Semantic Information Processing Programs 
II. Significance of Current Difficulties 

Conclusion 61 

Part II. Assumptions Underlying Persistent 
Optimism 

Introduction 67 

3. The Biological Assumption 71 

4. The Psychological Assumption 75 

ix 



I. Empirical Evidence for the Psychological Assumption: 
Critique of the Scientific Methodology of Cognitive 
Simulation 

II. A Priori Arguments for the Psychological Assumption 

5. The Epistemological Assumption 101 

I. A Mistaken Argument from the Success of Physics 
H. A Mistaken Argument from the Success of Modern 
Linguistics 

6. The Ontological Assumption 118 

Conclusion 137 

Part III. Alternatives to the Traditional 
Assumptions 

Introduction 143 

7. The Role of the Body in Intelligent Behavior 147 

8. The Situation: Orderly Behavior Without Recourse to 

Rules 168 

9. The Situation as a Function of Human Needs 184 

Conclusion 193 

CONCLUSION: The Scope and Limits of 
Artificial Reason 

The Limits of Artificial Intelligence 197 

The Future of Artificial Intelligence 

Notes 221 

Index 253 



Preface 



In choosing to dissect artificial intelligence, Hubert Dreyfus has un- 
dertaken an inquiry of great public importance. This branch of science 
is seen by its practitioners as the basis for much more powerful versions 
of the computer technology that already pervades our society. As anyone 
can see who reads the daily press, many people are torn between hopes 
and fears aroused by digital computers, which they find mostly incom- 
prehensible and whose import therefore they cannot judge. But, as 
science lays claim to public support, so the public has a claim on critical 
analyses of science. 

Dreyfus serves all of us in venturing into an arcane technical field as 
a critical layman, a professional philosopher committed to questioning 
and analyzing the foundations of knowledge. Far, therefore, from shun- 
ning him as an unwelcome intruder or pelting him with acrimonious 
invective, artificial intelligence should welcome Dreyfus, draw on his 
correct insights, and set him right publicly, gently but ever so firmly, 
where misunderstandings or incomprehensions might flaw his logic. 
Dreyfus raises important and fundamental questions. One might there- 
fore expect the targets of his criticisms to react with greater human 
intelligence than when they simply shouted loud in response to his earlier 
sallies. The issues deserve serious public debate. They are too scientific 
to be left to philosophers and too philosophical to be left to scientists. 

Dreyfus sees agonizingly slow progress in all fundamental work on 

xi 



artificial intelligence. This he interprets as a sign of impenetrable barri- 
ers, rather than as the normal price for overcoming enormous technical 
and conceptual difficulties on the way to inevitable success. He sees 
artificial intelligence as limited by its assumption that the world is expli- 
cable in terms of elementary atomistic concepts, in a tradition traceable 
back to the Greeks. This insight challenges not only contemporary 
science and technology but also some of the foundations of Western 
philosophy. He puts in question the basic role that rules play in accepted 
ideas of what constitutes a satisfactory scientific explanation. Thereby he 
strikes at far more than the ability in principle of digital computers 
bound as they are to follow rules to exhibit intelligence of a kind which, 
according to his analysis, cannot be explained according to Kantian 
rules. 

He is too modern to ask his questions from a viewpoint that assumes 
that man and mind are somehow set apart from the physical universe and 
therefore not within reach of science. Quite to the contrary, he states 
explicitly his assumption that "there is no reason why, in principle, one 
could not construct an artificial embodied agent if one used components 
sufficiently like those which make up a human being." Instead, he points 
out that his questions are "philosophically interesting only if we restrict 
ourselves to asking if one can make such a robot by using a digital 
computer." Curiously enough to this technologist, Dreyfus's own philo- 
sophical arguments lead him to see digital computers as limited not so 
much by being mindless, as by having no body. 

This conclusion emerges from the contrast between the ability of 
human beings to "zero in" on relevant features of their environment 
while ignoring myriad irrelevancies and the enormous and admitted 
difficulty of artificial intelligence in determining what is relevant when 
the environment presented to a digital computer has not, in some way, 
been artificially constrained. The central statement of this theme is that 
"a person experiences the objects of the world as already interrelated and 
full of meaning. There is no justification for the assumption that we first 
experience isolated facts or snapshots of facts or momentary views of 
snapshots of isolated facts and then give them significance. This is the 
point that contemporary philosophers such as Heidegger and Wittgen- 



rrerace 

stein are trying to make/' The burden of artificial intelligence is indeed 
its apparent need to proceed in futility from the atom to the whole. 
People, on the other hand, effectively seem to perceive first a whole and 
only then, if necessary, analyze it into atoms. This, Dreyfus argues 
following Merleau-Ponty, is a consequence of our having bodies capable 
of an ongoing but unanalyzed mastery of their environment. 

Either Dreyfus's position or that of artificial intelligence might some 
day be corroborated or destroyed by new evidence from artificial intelli- 
gence itself, from psychology, neurophysiology, or other related disci- 
plines. Unless and until this happens, Dreyfus's work will stand for the 
layman as a lucid analysis of a difficult matter of great public moment. 
To the computer scientist concerned with progress in his specialty and 
with deeper understanding of the world, Dreyfus presents a profound 
challenge to the widespread idea that "knowledge consists of a large store 
of neutral data." Dreyfus clearly is not neutral. 

Anthony G. Oettinger 

Aiken Computation Laboratory 
Harvard University 



Acknowledgments 



The occasional acknowledgments scattered throughout the following 
pages only begin to reflect my indebtedness to a host of sympathetic and 
critical readers who not only weeded out mistakes but made many sub- 
stantive suggestions. Without the help of Ned Block, Susan Carey Block, 
Burton Bloom, Stuart Dreyfus, John Haugeland, Terrance Malick, An- 
thony Oettinger, Seymour Papert, George Rey, Charles Taylor, and 
Samuel Todes, this book would have been published much sooner and 
been easier to read, but also easier to dismiss. 

I am grateful to the American Council of Learned Societies, the Na- 
tional Science Foundation, and the RAND Corporation for supporting 
various stages of my research and writing, and to the Study Group on 
the Unity of Knowledge for enabling me to organize colloquia on several 
topics which subsequently found their way into the book. 

I also want to thank Rena Lieb for debugging an early version of the 
manuscript, and especially Barbara Behrendt, who deciphered the first 
draft and helped in more ways than I can mention. 



Introduction 



Since the Greeks invented logic and geometry, the idea that all reasoning 
might be reduced to some kind of calculation so that all arguments 
could be settled once and for all has fascinated most of the Western 
tradition's rigorous thinkers. Socrates was the first to give voice to this 
vision. The story of artificial intelligence might well begin around 450 
B.C. when (according to Plato) Socrates demands of Euthyphro, a fellow 
Athenian who, in the name of piety, is about to turn in his own father 
for murder: "I want to know what is characteristic of piety which makes 
all actions pious . . . that I may have it to turn to, and to use as a standard 
whereby to judge your actions and those of other men. >M Socrates is 
asking Euthyphro for what modern computer theorists would call an 
"effective procedure," "a set of rules which tells us, from moment to 
moment, precisely how to behave.'* 2 

Plato generalized this demand for moral certainty into an epistemolog- 
ical demand. According to Plato, all knowledge must be stateable in 
explicit definitions which anyone could apply. If one could not state his 
know-how in terms of such explicit instructions if his knowing how 



Notes begin on p. 221. [Citations are indicated by a superior figure. Substantive notes 
are indicated by a superior figure and an asterisk.] 

/ XV 



Introduction / xvl 

could not be converted into knowing that it was not knowledge but 
mere belief. According to Plato, cooks, for example, who proceed by 
taste and intuition, and poets who work from inspiration, have no knowl- 
edge: what they do does not involve understanding and cannot be under- 
stood. More generally, what cannot be stated explicitly in precise 
instructions all areas of human thought which require skill, intuition, 
or a sense of tradition are relegated to some kind of arbitrary fum- 
bling. 

But Plato was not yet fully a cyberneticist (although according to 
Norbert Wiener he was the first to use the term), for Plato was looking 
for semantic rather than syntactic criteria. His rules presupposed that 
the person understood the meanings of the constitutive terms. In the 
Republic Plato says that Understanding (the rulelike level of his divided 
line representing all knowledge) depends on Reason, which involves a 
dialectical analysis and ultimately an intuition of the meaning of the 
fundamental concepts used in understanding. Thus Plato admits his 
instructions cannot be completely formalized. Similarly, a modern com- 
puter expert, Marvin Minsky, notes, after tentatively presenting a Pla- 
tonic notion of effective procedure: "This attempt at definition is subject 
to the criticism that the interpretation of the rules is left to depend on 
some person or agent." 3 

Aristotle, who differed with Plato in this as in most questions concern- 
ing the application of theory to practice, noted with satisfaction that 
intuition was necessary to apply the Platonic rules: 

Yet it is not easy to find a formula by which we may determine how far and up 
to what point a man may go wrong before he incurs blame. But this difficulty 
of definition is inherent in every object of perception; such questions of degree 
are bound up with the circumstances of the individual case, where our only 
criterion is the perception. 4 

For the Platonic project to reach fulfillment one breakthrough is 
required: all appeal to intuition and judgment must be eliminated. As 
Galileo discovered that one could find a pure formalism for describing 
physical motion by ignoring secondary qualities and teleological consid- 
erations, so, one might suppose, a Galileo of human behavior might 



Introduction / xvii 

succeed in reducing all semantic considerations (appeal to meanings) to 
the techniques of syntactic (formal) manipulation. 

The belief that such a total formalization of knowledge must be possi- 
ble soon came to dominate Western thought. It already expressed a basic 
moral and intellectual demand, and the success of physical science 
seemed to imply to sixteenth-century philosophers, as it still seems to 
suggest to thinkers such as Minsky, that the demand could be satisfied. 
Hobbes was the first to make explicit the syntactic conception of thought 
as calculation: "When a man reasons, he does nothing else but conceive 
a sum total from addition of parcels," he wrote, "for REASON ... is 
nothing but reckoning. . . ." 5 

It only remained to work out the univocal parcels or "bits" with which 
this purely syntactic calculator could operate; Leibniz, the inventor of 
the binary system, dedicated himself to working out the necessary unam- 
biguous formal language. 

Leibniz thought he had found a universal and exact system of nota- 
tion, an algebra, a symbolic language, a "universal characteristic" by 
means of which "we can assign to every object its determined character- 
istic number." 6 In this way all concepts could be analyzed into a small 
number of original and undefined ideas; all knowledge could be ex- 
pressed and brought together in one deductive system. On the basis of 
these numbers and the rules for their combination all problems could be 
solved and all controversies ended: "if someone would (doubt my re- 
sults," Leibniz said, "I would say to him: 'Let us calculate, Sir,' and thus 
by taking pen and ink, we should settle the question." 7 

Like a modern computer theorist announcing a program about to be 
written, Leibniz claims: 

Since, however, the wonderful interrelatedness of all things makes it extremely 
difficult to formulate explicitly the characteristic numbers of individual things, 
I have invented an elegant artifice by virtue of which certain relations may be 
represented and fixed numerically and which may thus then be further deter- 
mined in numerical calculation. 8 

Nor was Leibniz reticent about the importance of his almost completed 
program. 



Introduction / xviii 

Once the characteristic numbers are established for most concepts, mankind will 
then possess a new instrument which will enhance the capabilities of the mind 
to far greater extent than optical instruments strengthen the eyes, and will 
supersede the microscope and telescope to the same extent that reason is superior 
to eyesight. 9 

With this powerful new tool, the skills which Plato could not formal- 
ize, and so treated as confused thrashing around, could be recuperated 
as theory. In one of his "grant proposals" his explanations of how he 
could reduce all thought to the manipulation of numbers if he had money 
enough and time Leibniz remarks: 

the most important observations and turns of skill in all sorts of trades and 
professions are as yet unwritten. This fact is proved by experience when passing 
from theory to practice we desire to accomplish something. Of course, we can 
also write up this practice, since it is at bottom just another theory more complex 
and particular. . . . lo 

Leibniz had only promises, but in the work of George Boole, a math- 
ematician and logician working in the early nineteenth century, his 
program came one step nearer to reality. Like Hobbes, Boole supposed 
that reasoning was calculating, and he set out to "investigate the funda- 
mental laws of those operations of the mind by which reasoning is 
performed, to give expression to them in the symbolic language of a 
Calculus. . . ."" 

Boolean algebra is a binary algebra for representing elementary logical 
functions. If "a" and "fr" represent variables, "." represents "and," 
" + " represents "or," and "1" and "0" represent "true" and "false" 
respectively, then the rules governing logical manipulation can be writ- 
ten in algebraic form as follows: 

<2 -h a = a a + = a a -f 1 = 1 
a- 0= a a- = a- 1 = a 

Western man was now ready to begin the calculation. 

Almost immediately, in the designs of Charles Babbage (1835), prac- 
tice began to catch up to theory. Babbage designed what he called an 
"Analytic Engine" which, though never built, was to function exactly 
like a modern digital computer, using punched cards, combining logical 



Introduction / xix 

and arithmetic operations, and making logical decisions along the way 
based upon the results of its previous computations. 

An important feature of Babbage's machine was that it was digital. 
There are two fundamental types of computing machines: analogue and 
digital. Analogue computers do not compute in the strict sense of the 
word. They operate by measuring the magnitude of physical quantities. 
Using physical quantities, such as voltage, duration, angle of rotation of 
a disk, and so forth, proportional to the quantity to be manipulated, they 
combine these quantities in a physical way and measure the result. A 
slide rule is a typical analogue computer. A digital computer as the 
word digit, Latin for "finger," implies represents all quantities by dis- 
crete states, for example, relays which are open or closed, a dial which 
can assume any one often positions, and so on, and then literally counts 
in order to get its result. 

Thus, whereas analogue computers operate with continuous quanti- 
ties, all digital computers are discrete state machines. As A. M. Turing, 
famous for defining the essence of a digital computer, puts it: 

[Discrete state machines] move by sudden jumps or clicks from one quite definite 
state to another. These states are sufficiently different for the possibility of 
confusion between them to be ignored. Strictly speaking there are no such 
machines. Everything really moves continuously. But there are many kinds of 
machines which can profitably be thought of as being discrete state machines. For 
instance in considering the switches for a lighting system it is a convenient fiction 
that each switch must be definitely on or definitely off. There must be intermedi- 
ate positions, but for most purposes we can forget about them. 12 

Babbage's ideas were too advanced for the technology of his time, for 
there was no quick efficient way to represent and manipulate the digits. 
He had to use awkward mechanical means, such as the position of 
cogwheels, to represent the discrete states. Electric switches, however, 
provided the necessary technological breakthrough. When, in 1944, H. 
H. Aiken actually built the first practical digital computer, it was elec- 
tromechanical using about 3000 telephone relays. These were still slow, 
however, and it was only with the next generation of computers using 
vacuum tubes that the modern electronic computer was ready. 

Ready for anything. For, since a digital computer operates with ab- 



Introduction / xx 

stract symbols which can stand for anything, and logical operations 
which can relate anything to anything, any digital computer (unlike an 
analogue computer) is a universal machine. First, as Turing puts it, it can 
simulate any other digital computer. 

This special property of digital computers, that they can mimic any discrete state 
machine, is described by saying that they are universal machines. The existence 
of machines with this property has the important consequence that, considera- 
tions of speed apart, it is unnecessary to design various new machines to do 
various computing processes. They can all be done with one digital computer, 
suitably programmed for each case. It will be seen that as a consequence of this 
all digital computers are in a sense equivalent. 13 

Second, and philosophically more significant, any process which can be 
formalized so that it can be represented as series of instructions for the 
manipulation of discrete elements, can, at least in principle, be repro- 
duced by such a machine. Thus even an analogue computer, provided 
that the relation of its input to its output can be described by a precise 
mathematical function, can be simulated on a digital machine. 14 * 

But such machines might have remained overgrown adding machines, 
had not Plato's vision, refined by two thousand years of metaphysics, 
found in them its fulfillment. At last here was a machine which operated 
according to syntactic rules, on bits of data. Moreover, the rules were 
built into the circuits of the machine. Once the machine was pro- 
grammed there was no need for interpretation; no appeal to human 
intuition and judgment. This was just what Hobbes and Leibniz had 
ordered, and Martin Heidegger appropriately saw in cybernetics the 
culmination of the philosophical tradition. 15 * 

Thus while practical men like Eckert and Mauchly, at the University 
of Pennsylvania, were designing the first electronic digital machine, theo- 
rists, such as Turing, trying to understand the essence and capacity of 
such machines, became interested in an area which had thus far been the 
province of philosophers: the nature of reason itself. 

In 1950, Turing wrote an influential article, "Computing Machinery 
and Intelligence," in which he points out that "the present interest in 
'thinking machines' has been aroused by a particular kind of machine, 
usually called an 'electronic computer' or a 'digital computer.' " 16 He 



Introduction / xxi 

then takes up the question "Can [such] machines think?" 

To decide this question Turing proposes a test which he calls the 
imitation game: 

The new form of the problem can be described in terms of a game which we call 
the "imitation game." It is played with three people, a man (A), a woman (B), 
and an interrogator (C) who may be of either sex. The interrogator stays in a 
room apart from the other two. The object of the game for the interrogator is 
to determine which of the other two is the man and which is the woman. He 
knows them by labels X and Y, and at the end of the game he says either "X 
is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put 
questions to A and B thus: 

C: Will X please tell me the length of his or her hair? Now suppose X is 
actually A, then A must answer. It is A's object in the game to try to cause C 
to make the wrong identification. His answer might therefore be 

"My hair is shingled, and the longest strands are about nine inches long." 

In order that tones of voice may not help the interrogator the answers should 
be written, or better still, typewritten. The ideal arrangement is to have a tele- 
printer communicating between the two rooms. Alternatively, the question and 
answers can be repeated by an intermediary. The object of the game for the third 
player (B) is to help the interrogator. The best strategy for her is probably to give 
truthful answers. She can add such things as "I am the woman, don't listen to 
him!" to her answers, but it will avail nothing as the man can make similar 
remarks. 

We now ask the question, "What will happen when a machine takes the part 
of A in this game?" Will the interrogator decide wrongly as often when the game 
is played like this as he does when the game is played between a man and a 
woman? These questions replace our original, "Can machines think?" 17 

This test has become known as the Turing Test. Philosophers may 
doubt whether merely behavioral similarity could ever give adequate 
ground for the attribution of intelligence, 18 but as a goal for those actually 
trying to construct thinking machines, and as a criterion for critics to use 
in evaluating their work, Turing's test was just what was needed. 

Of course, no digital computer immediately volunteered or was 
drafted for Turing's game. In spite of its speed, accuracy, and universal- 
ity, the digital computer was still nothing more than a general-symbol 
manipulating device. The chips, however, were now down on the old 
Leibnizian bet. The time was ripe to produce the appropriate symbolism 



Introduction / xxii 

and the detailed instructions by means of which the rules of reason could 
be incorporated in a computer program. Turing had grasped the possibil- 
ity and provided the criterion for success, but his article ended with only 
the sketchiest suggestions about what to do next: 

We may hope that machines will eventually compete with men in all purely 
intellectual fields. But which are the best ones to start with? Even this is a difficult 
decision. Many people think that a very abstract activity, like the playing of 
chess, would be best. It can also be maintained that it is best to provide the 
machine with the best sense organs that money can buy, and then teach it to 
understand and speak English. This process could follow the normal teaching of 
a child. Things would be pointed out and named, etc. Again I do not know what 
the right answer is, but I think both approaches should be tried. 19 

A technique was still needed for finding the rules which thinkers from 
Plato to Turing assumed must exist a technique for converting any 
practical activity such as playing chess or learning a language into the 
set of instructions Leibniz called a theory. Immediately, as if following 
Turing's hints, work got under way on chess and language. The same 
year Turing wrote his article, Claude E. Shannon, the inventor of infor- 
mation theory, wrote an article on chess-playing machines in which he 
discussed the options facing someone trying to program a digital com- 
puter to play chess. 

Investigating one particular line of play for 40 moves would be as bad as investi- 
gating all lines for just two moves. A suitable compromise would be to examine 
only the important possible variations that is, forcing moves, captures and 
main threats and cany out the investigation of the possible moves far enough 
to make the consequences of each fairly clear. It is possible to set up some rough 
criteria for selecting important variations, not as efficiently as a chess master, but 
sufficiently well to reduce the number of variations appreciably and thereby 
permit a deeper investigation of the moves actually considered. 20 

Shannon did not write a chess program, but he believed that "an elec- 
tronic computer programmed in this manner would play a fairly strong 
game at speeds comparable to human speeds." 21 

In 1955 Allen Newell wrote a sober survey of the problems posed by 
the game of chess and suggestions as to how they might be met. Newell 
notes that "These [suggested] mechanisms are so complicated that it is 



Introduction / xxm 

impossible to predict whether they will work." 22 The next year, however, 
brought startling success. A group at Los Alamos produced a program 
which played poor but legal chess on a reduced board. In a review of this 
work, Allen Newell, J. C. Shaw, and H. A. Simon concluded: "With very 
little in the way of complexity, we have at least entered the arena of 
human play we can beat a beginner." 23 And by 1957, Alex Bernstein 
had a program for the IBM 704 which played two "passable amateur 
games." 24 

Meanwhile, Anthony Oettinger was working on the other Turing line. 
Having already in 1952 programmed a machine which simulated simple 
conditioning, increasing or decreasing a set response on the basis of 
positive or negative reinforcement, Oettinger turned to the problem of 
language translation and programmed a Russian-English mechanical 
dictionary. Further research in these directions, it seemed, might lead to 
a computer which could be taught to associate words and objects. 

But neither of these approaches offered anything like a general theory 
of intelligent behavior. What was needed were rules for converting any 
sort of intelligent activity into a set of instructions. At this point Herbert 
Simon and Allen Newell, analyzing the way a student proceeded to solve 
logic problems, noted that their subjects tended to use rules or shortcuts 
which were not universally correct, but which often helped, even if they 
sometimes failed. Such a rule of thumb might be, for example: always 
try to substitute a shorter expression for a longer one. Simon and Newell 
decided to try to simulate this practical intelligence. The term "heuristic 
program" was used to distinguish the resulting programs from programs 
which are guaranteed to work, so-called algorithmic programs which 
follow an exhaustive method to arrive at a solution, but which rapidly 
become unwieldy when dealing with practical problems. 

This notion of a rule of practice provided a breakthrough for those 
looking for a way to program computers to exhibit general problem- 
solving behavior. Something of the excitement of this new idea vibrates 
in the first paragraph of Newell, Shaw, and Simon's classic article "Em- 
pirical Explorations with the Logic Theory Machine: A Case Study in 
Heuristics." 



Introduction / xxiv 

This is a case study in problem-solving, representing part of a program of 
research on complex information-processing systems. We have specified a system 
for finding proofs of theorems in elementary symbolic logic, and by programming 
a computer to these specifications, have obtained empirical data on the problem- 
solving process in elementary logic. The program is called the Logic Theory 
Machine (LT); it was devised to learn how it is possible to solve difficult problems 
such as proving mathematical theorems, discovering scientific laws from data, 
playing chess, or understanding the meaning of English prose. 

The research reported here is aimed at understanding the complex processes 
(heuristics) that are effective in problem-solving. Hence, we are not interested in 
methods that guarantee solutions, but which require vast amounts of computa- 
tion. Rather, we wish to understand how a mathematician, for example, is able 
to prove a theorem even though he does not know when he starts how, or if, he 
is going to succeed. 25 

But Newell and Simon soon realized that even this approach was not 
general enough. The following year (1957) they sought to abstract the 
heuristics used in the logic marine, and apply them to a range of similar 
problems. This gave rise to a program called the General Problem Solver 
or GPS. The motivation and orientation of the work on the General 
Problem Solver are explained in Newell, Shaw, and Simon's first major 
report on the enterprise. 

This paper ... is part of an investigation into the extremely complex processes 
that are involved in intelligent, adaptive, and creative behavior. . . . 

Many kinds of information can aid in solving problems: information may 
suggest the order in which possible solutions should be examined; it may rule 
out a whole class of solutions previously thought possible; it may provide a cheap 
test to distinguish likely from unlikely possibilities; and so on. All these kinds 
of information are heuristics things that aid discovery. Heuristics seldom pro- 
vide infallible guidance. . . . Often they "work," but the results are variable and 
success is seldom guaranteed. 26 

To convey a sense of the general heuristics their program employed, 
Newell and Simon introduced an example of everyday intelligent be- 
havior: 

I want to take my son to nursery school. What's the difference between what I 
have and what I want? One of distance. What changes distance? My automobile. 
My automobile won*t work. What's needed to make it work? A new battery. 
What has new batteries? An auto repair shop. I want the repair shop to put in 



Introduction / xxv 

a new battery; but the shop doesn't know I need one. What is the difficulty? One 
of communication. What allows communication? A telephone. . . . And so on. 
This kind of analysis classifying things in terms of the functions they serve, 
and oscillating among ends, functions required, and means that perform them 
forms the basic system of heuristic of GPS. More precisely, this means-end 
system of heuristic assumes the following: 

1 . If an object is given that is not the desired one, differences will be detectable 
between the available object and the desired object. 

2. Operators affect some features of their operands and leave others un- 
changed. Hence operators can be characterized by the changes they produce and 
can be used to try to eliminate differences between the objects to which they are 
applied and desired objects. 

3. Some differences will prove more difficult to affect than others. It is profita- 
ble, therefore, to try to eliminate "difficult" differences, even at the cost of 
introducing new differences of lesser difficulty. This process can be repeated as 
long as progress is being made toward eliminating the more difficult differences. 27 

With digital computers solving such problems as how to get three 
cannibals and three missionaries across a river without the cannibals 
eating the missionaries, it seemed that finally philosophical ambition had 
found the necessary technology: that the universal, high-speed computer 
had been given the rules for converting reasoning into reckoning. Simon 
and Newell sensed the importance of the moment and jubilantly an- 
nounced that the era of intelligent machines was at hand. 

We have begun to learn how to use computers to solve problems, where we do 
not have systematic and efficient computational algorithms. And we now know, 
at least in a limited area, not only how to program computers to perform such 
problem-solving activities successfully; we know also how to program computers 
to learn to do these things. 

In short, we now have the elements of a theory of heuristic (as contrasted with 
algorithmic) problem solving; and we can use this theory both to understand 
human heuristic processes and to simulate such processes with digital computers. 
Intuition, insight, and learning are no longer exclusive possessions of humans: 
any large high-speed computer can be programmed to exhibit them also. 28 

This field of research, dedicated to using digital computers to simulate 
intelligent behavior, soon came to be known as "artificial intelligence." 
One should not be misled by the name. No doubt an artificial nervous 
system sufficiently like the human one, with other features such as sense 



Introduction / xxvi 

organs and a body, would be intelligent. But the term "artificial" does 
not mean that workers in artificial intelligence are trying to build an 
artificial man. Given the present state of physics, chemistry, and neuro- 
physiology, such an undertaking is not feasible. Simon and the pioneers 
of artificial intelligence propose to produce something more limited: a 
heuristic program which will enable a digital information-processing 
machine to exhibit intelligence. 

Likewise, the term "intelligence" can be misleading. No one expects 
the resulting robot to reproduce everything that counts as intelligent 
behavior in human beings. It need not, for example, be able to pick a 
good wife, or get across a busy street. It must only compete in the more 
objective and disembodied areas of human behavior, so as to be able to 
win at Turing's game. 

This limited objective of workers in artificial intelligence is just what 
gives such work its overwhelming significance. These last metaphysicians 
are staking everything on man's ability to formalize his behavior; to 
bypass brain and body, and arrive, all the more surely, at the essence of 
rationality. 

Computers have already brought about a technological revolution 
comparable to the Industrial Revolution. If Simon is right about the 
imminence of artificial intelligence, they are on the verge of creating an 
even greater conceptual revolution a change in our understanding of 
man. Everyone senses the importance of this revolution, but we are so 
near the events that it is difficult to discern their significance. This much, 
however, is clear. Aristotle defined man as a rational animal, and since 
then reason has been held to be of the essence of man. If we are on the 
threshold of creating artificial intelligence we are about to see the tri- 
umph of a very special conception of reason. Indeed, if reason can be 
programmed into a computer, this will confirm an understanding of the 
nature of man, which Western thinkers have been groping toward for 
two thousand years but which they only now have the tools to express 
and implement. The incarnation of this intuition will drastically change 
our understanding of ourselves. If, on the other hand, artificial intelli- 
gence should turn out to be impossible, then we will have to distinguish 



Introduction / xxvii 

human from artificial reason, and this too will radically change our view 
of ourselves. Thus the moment has come either to face the truth of the 
tradition's deepest intuition or to abandon what has passed for an under- 
standing of man's nature for two thousand years. 

Although it is perhaps too early for a full answer, we must make an 
attempt to determine the scope and limits of the sort of reason which has 
come fully into force since the perfection of the "analytical engine." We 
must try to understand to what extent artificial intelligence is possible, 
and if there are limits to the possibility of computer simulation of intelli- 
gent behavior, we must determine those limits and their significance. 
What we learn about the limits of intelligence in computers will tell us 
something about the character and extent of human intelligence. What 
is required is nothing less than a critique of artificial reason. 

II 

The need for a critique of artificial reason is a special case of a general 
need for critical caution in the behavioral sciences. Chomsky remarks 
that in these sciences "there has been a natural but unfortunate tendency 
to 'extrapolate,' from the thimbleful of knowledge that has been attained 
in careful experimental work and rigorous data-processing, to issues of 
much wider significance and of great social concern." He concludes 
that 

the experts have the responsibility of making clear the actual limits of their 
understanding and of the results they have so far achieved. A careful analysis 
of these limits will demonstrate that in virtually every domain of the social and 
behavioral sciences the results achieved to date will not support such "extrapola- 
tion." 29 

Artificial intelligence, at first glance, seems to be a happy exception to 
this pessimistic principle. Every day we read that digital computers play 
chess, translate languages, recognize patterns, and will soon be able to 
take over our jobs. In fact this now seems like child's play. Literally! In 
a North American Newspaper Alliance release, dated December 1968, 
entitled "A Computer for Kids" we are told that 



Introduction / xxviii 

Cosmos, the West German publishing house . . . has come up with a new idea 
in gifts. . . . It's a genuine (if small) computer, and it costs around $20. Battery 
operated, it looks like a portable typewriter. But it can be programmed like any 
big computer to translate foreign languages, diagnose illnesses, even provide a 
weather forecast. 

And in a recent Life Magazine article (Nov. 20, 1970) entitled "Meet 
Shakey, The First Electronic Person," the wide-eyed reader is told of a 
computer "made up of five major systems of circuitry that correspond 
quite closely to basic human faculties sensation, reason, language, 
memory [and] ego." According to the article, this computer "sees," 
"understands," "learns," and, in general, has "demonstrated that ma- 
chines can think." Several distinguished computer scientists are quoted 
as predicting that in from three to fifteen years "we will have a machine 
with the general intelligence of an average human being . . . and in a few 
months [thereafter] it will be at genius level. . . ." 

The complete robot may be a few years off, of course, but anyone 
interested in the prospective situation at the turn of the century can see 
in the film 2001: A Space Odyssey a robot named HAL who is cool, 
conversational, and very nearly omniscient and omnipotent. And this 
film is not simply science-fiction fantasy. A Space Odyssey was made with 
scrupulous documentation. The director, Stanley Kubrick, consulted the 
foremost computer specialists so as not to be misled as to what was at 
least remotely possible. Turing himself had in 1950 affirmed his belief 
that "at the end of the century the use of words and general educated 
opinion will have altered so much that one will be able to speak of 
machines thinking without expecting to be contradicted." 30 And the 
technical consultant for the film, Professor Marvin Minsky, working on 
an early prototype of HAL in his laboratory at M.I.T., assured Kubrick 
that Turing was, if anything, too pessimistic. 

That Minsky was not misunderstood by Kubrick is clear from Min- 
sky's editorial for Science Journal which reads like the scenario for 
2007: 

At first machines had simple claws. Soon they will have fantastically graceful 
articulations. Computers' eyes once could sense only a hole in a card. Now they 
recognize shapes on simple backgrounds. Soon they will rival man's analysis of 



Introduction / X xix 

his environment. Computer programs once merely added columns of figures. 
Now they play games well, understand simple conversations, weigh many factors 
in decisions. What next? 

Today, machines solve problems mainly according to the principles we build 
into them. Before long, we may learn how to set them to work upon the very 
special problem of improving their own capacity to solve problems. Once a 
certain threshold is passed, this could lead to a spiral of acceleration and it may 
be hard to perfect a reliable 'governor' to restrain it. 31 

It seems that there may be no limit to the range and brilliance of the 
properly programmed computer. It is no wonder that among philoso- 
phers of science one finds an assumption that machines can do every- 
thing people can do, followed by an attempt to interpret what this bodes 
for the philosophy of mind; while among moralists and theologians one 
finds a last-ditch retrenchment to such highly sophisticated behavior as 
moral choice, love, and creative discovery, claimed to be beyond the 
scope of any machine. Thinkers in both camps have failed to ask the 
preliminary question whether machines can in fact exhibit even elemen- 
tary skills like playing games, solving simple problems, reading simple 
sentences and recognizing patterns, presumably because they are under 
the impression, fostered by the press and artificial-intelligence research- 
ers such as Minsky, that the simple tasks and even some of the most 
difficult ones have already been or are about to be accomplished. To 
begin with, then, these claims must be examined. 

It is fitting to begin with a prediction made by Herbert Simon in 1957 
as his General Problem Solver seemed to be opening up the era of 
artificial intelligence: 

It is not my aim to surprise or shock you. . . . But the simplest way I can 
summarize is to say that there are now in the world machines that think, that 
learn and that create. Moreover, their ability to do these things is going to 
increase rapidly until in a visible future the range of problems they can han- 
dle will be coextensive with the range to which the human mind has been ap- 
plied. 

Simon then predicts, among other things, 

1. That within ten years a digital computer will be the world's chess champion, 
unless the rules bar it from competition. 



Introduction / xxx 

2. That within ten years a digital computer will discover and prove an impor- 
tant new mathematical theorem. 

3. That within ten years most theories in psychology will take the form of 
computer programs, or of qualitative statements about the characteristics of 
computer programs. 32 

Unfortunately, the tenth anniversary of this historic talk went unno- 
ticed, and workers in artificial intelligence did not, at any of their many 
national and international meetings, take time out from their progress 
reports to confront these predictions with the actual achievements. Now 
fourteen years have passed, and we are being warned that it may soon 
be difficult to control our robots. It is certainly high time to measure this 
original prophecy against reality. 

Already in the five years following Simon's predictions, publications 
suggested that the first of Simon's forecasts had been half-realized, and 
that considerable progress had been made in fulfilling his second predic- 
tion. This latter, the theorem-discovery prediction, was "fulfilled" by W. 
R. Ashby (one of the leading authorities in the field) when, in a review 
of Feigenbaum and Feldman's anthology Computers and Thought, he 
hailed the mathematical power of the properly programmed computer: 
"Gelernter's theorem-proving program has discovered a new proof of the 
pons asinorum that demands no construction." This proof, Dr. Ashby 
goes on to say, is one which "the greatest mathematicians of 2000 years 
have failed to notice . ." . which would have evoked the highest praise had 
it occurred." 33 

The theorem sounds important, and the naive reader cannot help 
sharing Ashby's enthusiasm. A little research, however, reveals that the 
pons asinorum, or ass's bridge, is the elementary theorem proved in 
Euclidian geometry namely that the opposite angles of an isosceles 
triangle are equal. Moreover, the first announcement of the "new" proof 
"discovered" by the machine is attributed to Pappus (A.D. 300). 34 There 
is a striking disparity between Ashby's excitement and the antiquity and 
simplicity of this proof. We are still a long way from "the important 
mathematical theorem" to be found by 1967. 

The chess-playing story is more involved and might serve as a model 
for a study of the production of intellectual smog in this area. In 1958, 



Introduction / xxxi 

the year after Simon's prediction, Newell, Shaw, and Simon presented 
an elaborate chess-playing program. As described in their classic paper, 
"Chess-Playing Programs and the Problem of Complexity," their pro- 
gram was "not yet fully debugged,*' so that one "cannot say very much 
about the behavior of the program." 35 Still, it is clearly "good in [the] 
. . . opening." 36 This is the last detailed published report on the program. 
In the same year, however, Newell, Shaw, and Simon announced: "We 
have written a program that plays chess," 37 and Simon, on the basis of 
this success, revised his earlier prediction: 

In another place, we have predicted that within ten years a computer will 
discover and prove an important mathematical theorem. On the basis of our 
experience with the heuristics of logic and chess, we are willing to add the further 
prediction that only moderate extrapolation is required from the capacities of 
programs already in existence to achieve the additional problem-solving power 
needed for such simulation. 38 

Public gullibility and Simon's enthusiasm was such that Newell, Shaw, 
and Simon's claims concerning their still bugged program were sufficient 
to launch the chess machine into the realm of scientific mythology. In 
1959, Norbert Wiener, escalating the claim that the program was "good 
in the opening," informed the NYU Institute of Philosophy that "chess- 
playing machines as of now will counter the moves of a master game with 
the moves recognized as right in the text books, up to some point in the 
middle game." 39 In the same symposium, Michael Scriven moved from 
the ambiguous claim that "machines now play chess" to the positive 
assertion that "machines are already capable of a good game." 40 

In fact, in its few recorded games, the Newell, Shaw, Simon program 
played poor but legal chess, and in its last official bout (October 1960) 
was beaten in 35 moves by a ten-year-old novice. Fact, however, had 
ceased to be relevant. 

While their program was losing its five or six poor games and the 
myth they had created was holding its own against masters in the middle 
game Newell, Shaw, and Simon kept silent. When they speak again, 
three years later, they do not report their difficulties and disappointment. 
Rather, as if to take up where the myth left off, Simon published an 
article in Behavioral Science announcing a program which played 



Introduction / xxxii 

"highly creative" chess end games involving "combinations as difficult 
as any that have been recorded in chess history." 41 That the program 
restricts these end games to dependence on continuing checks, so that 
the number of relevant moves is greatly reduced, is mentioned but not 
emphasized. On the contrary, it is misleadingly implied that similar 
simple heuristics would account for master play even in the middle 
game/ 2 * Thus, the article gives the impression that the chess prediction 
is almost realized. With such progress, the chess championship may be 
claimed at any moment. Indeed, a Russian cyberneticist, upon hearing 
of Simon's ten-year estimate, called it "conservative." 43 And Fred 
Gruenberger at RAND suggested that a world champion is not enough 
that we should aim for "a program which plays better than any man 
could." 44 This regenerating confusion makes one think of the mythical 
French beast which is supposed to secrete the fog necessary for its own 
respiration. 

Reality comes limping along behind these impressive pronounce- 
ments. Embarrassed by my expose of the disparity between their enthusi- 
asm and their results, AI workers finally produced a reasonably 
competent program. R. Greenblatt's program called MacHack did in 
fact beat the author, 45 * a rank amateur, and has been entered in several 
tournaments in which it won a few games. This limited success revived 
hopes and claims. Seymour Papert, the second in command at the M.I.T. 
robot project, leaped in to defend Simon's prediction, asserting that "as 
a statement of what researchers in the field consider to be a possible goal 
for the near future, this is a reasonable statement." 46 And on page 1 of 
the October 1968 issue of Science Journal, Donald Michie, the leader of 
England's artificial intelligentsia, writes that "today machines can play 
chess at championship level." 47 However, chess master de Groot, discuss- 
ing the earlier chess programs, once said: "programs are still very poor 
chess players and I do not have much hope for substantial improvement 
in the future." And another chess master, Eliot Hearst, discussing the 
M.I.T. program in Psychology Today, adds: "De Groot's comment was 
made in 1964 and MacHack's recent tournament showing would not 
require him to revise his opinion." 48 Nor would most recent events. 
Greenblatt's program has been gradually improved, but it seems to have 



Introduction / xxxiii 

reached a point of saturation. During the past two years, it lost all games 
in the tournaments in which it had been entered, and received no further 
publicity. We shall soon see that given the limitations of digital comput- 
ers this is just what one would expect. 

It is to Greenblatt's credit that even in the heyday of MacHack he 
made no prediction; as for Simon and the world championship, the ten 
years are well up, and the computer is at best a class C amateur. 49 * 

This rapid rundown of the state of the art vis-a-vis two of Simon's 
predictions has, I hope, cleared the air. It is essential to be aware at the 
outset that despite predictions, press releases, films, and warnings, artifi- 
cial intelligence is a promise and not an accomplished fact. Only then can 
we begin our examination of the actual state and future hopes of artificial 
intelligence at a sufficiently rudimentary level. 

The field of artificial intelligence has many divisions and subdivisions, 
but the most important work can be classified into four areas: game 
playing, language translating, problem solving, and pattern recognition. 
We have already discussed the state of game-playing research. We shall 
now look at the work in the remaining three fields in detail. In Part I 
my general thesis will be that the field of artificial intelligence exhibits 
a recurring pattern: early, dramatic success followed by sudden unex- 
pected difficulties. This pattern occurs in all four areas, in two phases 
each lasting roughly five years. The work from 1957 to 1962 (Chapter 
1), is concerned primarily with Cognitive Simulation (fcS) the use of 
heuristic programs to simulate human behavior by attempting to re- 
produce the steps by which human beings actually proceed. The second 
period (Chapter 2) is predominantly devoted to semantic information 
processing. This is artificial intelligence in a narrower sense than I have 
been using the term thus far. AI (for this restricted sense I shall use the 
initials) is the attempt to simulate human intelligent behavior using 
programming techniques which need bear little or no resemblance to 
human mental processes. The difficulties confronting this approach have 
just begun to emerge. The task of the rest of Part I is to discover the 
underlying common source of all these seemingly unconnected setbacks. 

These empirical difficulties, these failures to achieve predicted prog- 



Introduction / xxxiv 

ress, never, however, discourage the researchers, whose optimism seems 
to grow with each disappointment. We therefore have to ask what as- 
sumptions underlie this persistent optimism in the face of repeated fail- 
ures. Part II attempts to bring to light four deeply rooted assumptions 
or prejudices which mask the gravity of the current impasse, and to lay 
bare the conceptual confusion to which these prejudices give rise. 

But these prejudices are so deeply rooted in our thinking that the only 
alternative to them seems to be an obscurantist rejection of the possibility 
of a science of human behavior. Part III attempts to answer this objec- 
tion, insofar as it can be answered, by presenting an alternative to these 
traditional assumptions, drawing on the ideas of twentieth-century 
thinkers whose work is an implicit critique of artificial reason, although 
it has not before been read in this light. 

We shall then, in the Conclusion, be in a position to characterize 
artificial reason and indicate its scope and limits. This in turn will enable 
us to distinguish among various forms of intelligent behavior and to 
judge to what extent each of these types of intelligent behavior is pro- 
grammable in practice and in principle. 

If the order of argument presented above and the tone of my opening 
remarks seem strangely polemical for an effort in philosophical analysis, 
I can only point out that, as we have already seen, artificial intelligence 
is a field in which the rhetorical presentation of results often substitutes 
for research, so that research papers resemble more a debater's brief than 
a scientific report. Such persuasive marshaling of facts can only be an- 
swered in kind. Thus the accusatory tone of Part I. In Part II, however, 
I have tried to remain as objective as possible in testing fundamental 
assumptions, although I know from experience that challenging these 
assumptions will produce reactions similar to those of an insecure be- 
liever when his faith is challenged. 

For example, the year following the publication of my first investiga- 
tion of work in artificial intelligence, the RAND Corporation held a 
meeting of experts in computer science to discuss, among other topics, 
my report. Only an "expurgated" transcript of this meeting has been 
released to the public, but even there the tone of paranoia which per- 
vaded the discussion is present on almost every page. My report is called 



Introduction / xxxv 

"sinister," "dishonest," "hilariously funny," and an "incredible misrep- 
resentation of history." When, at one point, Dr. J. C. R. Licklider, then 
of IBM, tried to come to the defense of my conclusion that work should 
be done on man-machine cooperation, Seymour Papert of M.I.T. re- 
sponded: 

I protest vehemently against crediting Dreyfus with any good. To state that you 
can associate yourself with one of his conclusions is unprincipled. Dreyfus* 
concept of coupling men with machines is based on thorough misunderstanding 
of the problems and has nothing in common with any good statement that might 
go by the same words. 50 

The causes of this panic-reaction should themselves be investigated, 
but that is a job for psychology, or the sociology of knowledge. However, 
in anticipation of the impending outrage I want to make absolutely clear 
from the outset that what I am criticizing is the implicit and explicit 
philosophical assumptions of Simon and Minsky and their co-workers, 
not their technical work. True, their philosophical prejudices and naivete 
distort their own evaluation of their results, but this in no way detracts 
from the importance and value of their research on specific techniques 
such as list structures, and on more general problems such as data-base 
organization and access, compatibility theorems, and so forth. The fun- 
damental ideas that they have contributed in these areas have not only 
made possible the limited achievements in artificial intelligence but have 
contributed to other more flourishing areas of computer science. 

In some restricted ways even AI can have, and presumably will have 
practical value despite what I shall try to show are its fundamental 
limitations. (I restrict myself to AI because it is not clear that naive 
Cognitive Simulation, as it is now practiced, can have any value at all, 
except perhaps as a striking demonstration of the fact that in behaving 
intelligently people do not process information like a heuristically pro- 
grammed digital computer.) An artifact could replace men in some tasks 
for example, those involved in exploring planets without performing 
the way human beings would and without exhibiting human flexibility. 
Research in this area is not wasted or foolish, although a balanced view 
of what can and cannot be expected of such an artifact would certainly 
be aided by a little philosophical perspective. 



PARTI 



TEN YEARS OF RESEARCH 



IN ARTIFICIAL INTELLIGENCE 



(1957-1967) 



1 



Phase I (1957-1962) Cognitive Simulation 



I. Analysis of Work in Language Translation, 
Problem Solving, and Pattern Recognition 

LANGUAGE TRANSLATION 

The attempts at language translation by computers had the earliest 
success, the most extensive and expensive research, and the most un- 
equivocal failure. It was soon clear that a mechanical dictionary could 
easily be constructed in which linguistic items, whether they were parts 
of words, whole words, or groups of words, could be processed indepen- 
dently and converted one after another into corresponding items in 
another language. Anthony Oettinger, the first to produce a mechanical 
dictionary (1954), recalls the climate of these early days: "The notion of 
. . . fully automatic high quality mechanical translation, planted by 
overzealous propagandists for automatic translation on both sides of the 
Iron Curtain and nurtured by the wishful thinking of potential users, 
blossomed like a vigorous weed." l This initial enthusiasm and the 
subsequent disillusionment provide a sort of paradigm for the field. It is 
aptly described by Bar-Hillel in his report "The Present Status of Auto- 
matic Translation of Languages." 

Notes begin on p. 221. [Citations are indicated by a superior figure. Substantive notes 
are indicated by a superior figure and an asterisk.] 

/ 3 



What Computers Can't Do / 4 

During the first year of the research in machine translation, a considerable 

amount of progress was made It created among many of the workers actively 

engaged in this field the strong feeling that a working system was just around 
the corner. Though it is understandable that such an illusion should have been 
formed at the time, it was an illusion. It was created ... by the fact that a large 

number of problems were rather readily solved It was not sufficiently realized 

that the gap between such output . . . and high quality translation proper was 
still enormous, and that the problems solved until then were indeed many but 
just the simplest ones whereas the "few" remaining problems were the harder 
ones very hard indeed. 2 

During the ten years following the development of a mechanical dic- 
tionary, five government agencies spent about $20 million on mechanical 
translation research. 3 In spite of journalistic claims at various moments 
that machine translation was at last operational, this research produced 
primarily a much deeper knowledge of the unsuspected complexity of 
syntax and semantics. As Oettinger remarks, "The major problem of 
selecting an appropriate target correspondent for a source word on the 
basis of context remains unsolved, as does the related one of establishing 
a unique syntactic structure for a sentence that human readers find 
unambiguous." 4 Oettinger concludes: "The outlook is grim for those who 
still cherish hopes for fully automatic high-quality mechanical transla- 
tion." 5 * 

That was in 1963. Three years later, a government report, Language 
and Machines, distributed by the National Academy of Sciences Na- 
tional Research Council, pronounced the last word on the translation 
boom. After carefully comparing human translations and machine 
products the committee concluded: 

We have already noted that, while we have machine-aided translation of general 
scientific text, we do not have useful machine translation. Furthermore, there is 
no immediate or predictable prospect of useful machine translation. 6 

Ten years have elapsed since the early optimism concerning machine 
translation. At that time, flight to the moon was still science fiction, and 
the mechanical secretary was just around the corner. Now we have 
landed on the moon, and yet machine translation of typed scientific texts 



Phase I (1957-1962) Cognitive Simulation / 5 

let alone spoken language and more general material is still over the 
horizon, and the horizon seems to be receding at an accelerating rate. 
Since much of the hope for robots like those of 2007, or for more modest 
servants, depends on the sort of understanding of natural language which 
is also necessary for machine translation, the conclusion of the National 
Academy of Sciences strikes at all predictions such as Minsky's that 
within a generation the problem of creating artificial intelligence will be 
substantially solved. 

PROBLEM SOLVING 

Much of the early work in the general area of artificial intelligence, 
especially work on game playing and problem solving, was inspired and 
dominated by the work of Newell, Shaw, and Simon at the RAND 
Corporation and at Carnegie Institute of Technology. 7 Their approach 
is called Cognitive Simulation (CS) because the technique generally em- 
ployed is to collect protocols from human subjects, which are then 
analyzed to discover the heuristics these subjects employ. 8 * A program 
is then written which incorporates similar rules of thumb. 

Again we find an early success: in 1957 Newell, Shaw, and Simon's 
Logic Theorist, using heuristically guided trial-and-error search, proved 
38 out of 52 theorems from Principle, Mathematics Two years later, 
another Newell, Shaw, and Simon program, the General Probl im Solver 
(GPS), using more sophisticated means-ends analysis, solved the "canni- 
bal and missionary" problem and other problems of similar com- 
plexity. 9 * 

In 1961, after comparing a machine trace (see Figure 1, p. 7) with a 
protocol and finding that they matched to some extent, Newell and 
Simon concluded rather cautiously: 

The fragmentary evidence we have obtained to date encourages us to think that 
the General Problem Solver provides a rather good first approximation to an 
information processing theory of certain kinds of thinking and problem-solving 
behavior. The processes of "thinking" can no longer be regarded as completely 
mysterious. 10 



What Computers Can't Do / 6 

Soon, however, Simon gave way to more enthusiastic claims: 

Subsequent work has tended to confirm [our] initial hunch, and to demonstrate 
that heuristics, or rules of thumb, form the integral core of human problem- 
solving processes. As we begin to understand the nature of the heuristics that 
people use in thinking the mystery begins to dissolve from such (heretofore) 
vaguely understood processes as "intuition'* and "judgment." 11 

But, as we have seen in the case of language translating, difficulties have 
an annoying way of reasserting themselves. This time, the "mystery" of 
judgment reappears in terms of the organizational aspect of the problem- 
solving programs. Already in 1961 at the height of Simon's enthusiasm, 
Minsky saw the difficulties which would attend the application of trial- 
and-error techniques to really complex problems: 

The simplest problems, e.g., playing tic-tac-toe or proving the very simplest 
theorems of logic, can be solved by simple recursive application of all the avail- 
able transformations to all the situations that occur, dealing with sub-problems 
in the order of their generation. This becomes impractical in more complex 
problems as the search space grows larger and each trial becomes more expensive 
in time and effort. One can no longer afford a policy of simply leaving one 
unsuccessful attempt to go on to another. For, each attempt on a difficult prob- 
lem will involve so much effort that one must be quite sure that, whatever the 
outcome, the effort will not be wasted entirely. One must become selective to the 
point that no trial is made without a compelling reason. . . , 12 

This, Minsky claims, shows the need for a planning program, but as he 
goes on to point out: 

Planning methods . . . threaten to collapse when the fixed sets of categories 
adequate for simple problems have to be replaced by the expressions of descrip- 
tive language. 13 

In "Some Problems of Basic Organization in Problem-Solving Pro- 
grams" (December 1962), Newell discusses some of the problems which 
arise in organizing the Chess Program, the Logic Theorist, and especially 
the GPS with a candor rare in the field, and admits that "most of [these 
problems] are unsolved to some extent, either completely, or because the 
solutions that have been adopted are still unsatisfactory in one way or 
another." 14 No further progress has been reported toward the successful 



Phase I (1957-1962) Cognitive Simulation / 1 



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REJECT 

GOAL 2 

GOAL 6 APPLY R8 TO Ll 
PRODUCES L3 -RDQ 

GOAL 7 TRANSFORM L3 INTO LO 
GOAL 8 ADD P TO L3 
REJECT 

GOAL 2 

GOAL 9 APPLY R7 TO Ll 

GOAL 10 CHANGE CONNECTIVE TO V IN LEFT Ll 
GOAL 11 APPLY R6 TO LEFT Ll 

PRODUCES L4 (-RV -P) (-RDQ) 

GOAL 12 APPLY R7 TO L4 

GOAL 13 CHANGE CONNECTIVE TO V IN RIGHT L4 
GOAL 14 APPLY R6 TO RIGHT L4 

PRODUCES L5 (~R V ~P) (R V Q) 

COAL 15 APPLY R7 TO L5 

GOAL 16 CHANGE SIGN OF LEFT RIGHT L5 
GOAL 17 APPLY R6 TO RIGHT L5 

PRODUCES L6 (~R V ~PK~R=>Q. 

GOAL 18 APPLY R7 TO L6 

GOAL 19 CHANGE CONNECTIVE TO Y 

IN RIGHT L6 
REJECT 

GOAL 16 

NOTHING MORE 

GOAL 13 

NOTHING MORE 

GOAL 10 

NOTHING MORE 

Figure 1 



What Computers Can't Do / 8 

hierarchical organization of heuristic programs. (Significantly, the great- 
est achievement in the field of mechanical theorem-proving, Wang's 
theorem-proving program, which proved in less than five minutes all 52 
theorems chosen by Newell, Shaw, and Simon, does not use heuristics.) 
Public admission that GPS was a dead end, however, did not come 
until much later. In 1967, the tenth anniversary of Simon's predictions, 
Newell (and Ernst) soberly, quietly, and somewhat ambiguously an- 
nounced that GPS was being abandoned. The preface to their paper 
reveals that peculiar mixture of impasse and optimism which we have 
begun to recognize as characteristic of the field: 

We have used the term "final" in several places above. This does not indicate 
any feeling that this document marks a terminus to our research on general 
problem solvers; quite the contrary is true. However, we do feel that this particu- 
lar aggregate of IPL-V code should be laid to rest. 15 

That GPS has collapsed under the weight of its own organization 
becomes clearer later in the monograph. The section entitled "History 
of GPS" concludes: 

One serious limitation of the expected performance of GPS is the size of the 
program and the size of its rather elaborate data structure. The program itself 
occupies a significant portion of the computer memory and the generation of new 
data structures during problem solving quickly exhausts the remaining memory. 
Thus GPS is only designed to solve modest problems whose representation is not 
too elaborate. Although larger computers' memories would alleviate the extrava- 
gances of GPS's use of memory, conceptual difficulties would remain. 16 

This curve from success to optimism to failure can be followed in 
miniature in the case of Gelernter's Geometry Theorem Machine (1959). 
Its early success with theorems like the ports asinorum gave rise to the 
first prediction to be totally discredited. In an article published in 1960, 
Gelernter explains the heuristics of his program and then concludes: 
"Three years ago, the dominant opinion was that the geometry machine 
would not exist today. And today, hardly an expert will contest the 
assertion that machines will be proving interesting theorems in number 
theory three years hence," that is, in 1963. 17 There has been no further 
word from Gelernter and no further progress in purely mechanical math- 



Phase I (1957-1962) Cognitive Simulation / 9 

ematics. No more striking example exists of an "astonishing" early 
success and an even more astonishing failure to follow it up. 

PATTERN RECOGNITION 

This field is discussed last because the resolution of the difficulties 
which have arrested development in game playing, language translation, 
and problem solving presupposes success in the field of pattern recogni- 
tion (which in turn suffers from each of the difficulties encountered in 
the other fields). As Selfridge and Neisser point out in their classic article 
"Pattern Recognition by Machine," 

a man is continually exposed to a welter of data from his senses, and abstracts 
from it the patterns relevant to his activity at the moment. His ability to solve 
problems, prove theorems and generally run his life depends on this type of 
perception. We suspect that until programs to perceive patterns can be devel- 
oped, achievements in mechanical problem-solving will remain isolated technical 
triumphs. 18 

There has as usual been some excellent early work. For example, the 
Lincoln Laboratory group under Bernard Gold produced a program for 
transliterating hand-sent Morse code. More recently, programs have 
been written for recognizing a limited set of handwritten words and 
printed characters in various type fonts. These all operate by searching 
for predetermined topological features of the characters to be recognized, 
and checking these features against preset or learned "definitions" of 
each letter in terms of these traits. The trick is to find relevant features, 
that is, those that remain generally invariant throughout variations of 
size and orientation, and other distortions. This approach has been sur- 
prisingly successful where recognition depends on a small number of 
specific traits. 

But none of these programs constitutes a breakthrough in pattern 
recognition. Each is a small engineering triumph, an ad hoc solution of 
a specific problem, without general applicability. As Murray Eden, who 
has done some of the best work in pattern recognition, summed up the 
situation in 1968: 



What Computers Can't Do / 10 

Where there have been successes in performing pattern-recognition tasks by 
mechanical means, the successes have rested on rules that were prescribed ad 
hoc, in the literal sense of that phrase; that is to say, the successful methods 
classify reliably that particular set of patterns for which the methods were 
designed, but are likely to lack any significant value for classifying any other set 
of patterns. 19 

Even in these special cases, as Selfridge and Neisser remark, "The only 
way the machine can get an adequate set of features is from a human 
programmer." 20 They thus conclude their survey of the field with a 
challenge rather than a prediction: 

The most important learning process of all is still untouched: No current pro- 
gram can generate test features of its own. The effectiveness of all of them is 
forever restricted by the ingenuity or arbitrariness of their programmers. We can 
barely guess how this restriction might be overcome. Until it is, 'artificial intelli- 
gence* will remain tainted with artifice. 21 

Even these remarks may be too optimistic, however, in their supposi- 
tion that the present problem is feature-generation. The relative success 
of the Uhr-Vossler program, which generates and evaluates its own 
operators, shows that this problem is partially soluble. 22 But as long as 
recognition depends on a limited set of features, whether ad hoc or 
general, preprogrammed or generated, mechanical recognition has gone 
about as far as it can go. The number of traits that can be looked up in 
a reasonable amount of time is limited, and present programs have 
already reached this technological limit. In a paper presented at the 
Hawaii International Conference on the Methodologies of Pattern Rec- 
ognition (1968), Laveen Kanal and B. Chandrasekaran summed up the 
impasse as follows: 

Obviously, the engineering approach has built in limitations. There is a certain 
level of complexity above which the engineer's bag of tricks fails to produce 
results. As an example while even multifont printed character recognition has 
been successfully handled, a satisfactory solution of cursive script recognition 
defies all attempts. Similarly there seems to be a fairly big jump between isolated 
speech recognition and continuous speech recognition. Those who have been 
hoping to model human recognition processes have also reached an impasse. It 
is probable that those problems which the engineers have found difficult to 
handle are precisely those which have to await more detailed understanding of 



Phase I (1957-1962) Cognitive Simulation / 11 

human recognition systems. In any case, these feelings of crisis are intimately 
related to those in other aspects of artificial intelligence: game playing and 
mechanical translation. 23 

Again we find the same pattern of optimism followed by disillusion- 
ment. Often the disillusioned do not even understand why their hopes 
have been dashed, and their questioning goes unheard amidst the prom- 
ises and announcements of small technological advances. Such a dis- 
senter is Vincent Giuliano, formerly of Arthur D. Little Corporation. If 
Giuliano had a more detailed and insightful account of what went wrong, 
he would be the Oettinger or Bar-Hillel of the pattern recognition field. 

Like many of my colleagues, I was in hot pursuit of ways to develop something 
we sometimes refer to as artificial intelligence. ... in the mid-fifties, many 
ambitious research projects were launched with the goal of clearly demonstrating 
the learning capabilities of computers so that they could translate idiomatically, 
carry on free and natural conversations with humans, recognize speech and print 
it out, and diagnose diseases. All of these activities involve the discovery and 
learning of complex patterns. 

Only a few years ago we really believed that ultimately computers could be 
given the entire task of solving such problems, if only we could find the master 
key to making them do so. 

Alas! I feel that many of the hoped-for objectives may well be porcelain eggs; 
they will never hatch, no matter how long heat is applied to them, because they 
require pattern discovery purely on the part of machines working alone. The 
tasks of discovery demand human qualities. 24 

Conclusion 

By 1962, if we are to judge by published results, an overall pattern had 
begun to take shape, although in some cases it was not recognized until 
later: an early, dramatic success based on the easy performance of simple 
tasks, or low-quality work on complex tasks, and then diminishing re- 
turns, disenchantment, and, in some cases, pessimism. This pattern is not 
the result of overenthusiastic pressure from eager or skeptical outsiders 
who demand too much too fast. The failure to produce is measured solely 
against the expectations of those working in the field. 

When the situation is grim, however, enthusiasts can always fall back 



What Computers Can't Do / 12 

on their own optimism. This tendency to substitute long-range for opera- 
tional programs slips out in Feigenbaum and Feldman's claim that "the 
forecast for progress in research in human cognitive processes is most 
encouraging." 25 The forecast always has been, but one wonders: how 
encouraging are the prospects? Feigenbaum and Feldman claim that 
tangible progress is indeed being made, and they define progress very 
carefully as "displacement toward the ultimate goal." 26 According to this 
definition, the first man to climb a tree could claim tangible progress 
toward reaching the moon. 

Rather than climbing blindly, it is better to look where one is going. 
It is time to study in detail the specific problems confronting work in 
artificial intelligence and the underlying difficulties that they reveal. 

II. The Underlying Significance of Failure to 
Achieve Predicted Results 

Negative results, provided one recognizes them as such, can be interest- 
ing. Diminishing achievement, instead of the predicted accelerating suc- 
cess, perhaps indicates some unexpected phenomenon. Perhaps we are 
pushing out on a continuum like that of velocity, where further accelera- 
tion costs more and more energy as we approach the speed of light, or 
perhaps we are instead facing a discontinuity, which requires not greater 
effort but entirely different techniques, as in the case of the tree-climbing 
man who tries to reach the moon. 

It seems natural to take stock of the field at this point, yet surprisingly 
no one has done so. If someone had, he might have found that each of 
the four areas considered presupposes a specific form of human "infor- 
mation processing" that enables human subjects in that area to avoid the 
difficulties an artificial "subject" must confront. This section will isolate 
these four human forms of "information processing" and contrast them 
with their machine surrogates. 

FRINGE CONSCIOUSNESS VS. HEURISTICALLY GUIDED SEARCH 

It is common knowledge that certain games can be worked through 
on present-day computers with present-day techniques games like nim 



Phase I (1957-1962) Cognitive Simulation / 13 

and tic-tac-toe can be programmed so that the machine will win or draw 
every time. Other games, however, cannot be solved in this way on 
present-day computers, and yet have been successfully programmed. In 
checkers, for example, it turns out that there are reliable ways to deter- 
mine the probable value of a move on the basis of certain parameters such 
as control of center position, advancement, and so forth. This, plus the 
fact that there are relatively few moves since pieces block each other and 
captures are forced, makes it possible to explore all plausible moves to 
a depth of as many as twenty moves, which proves sufficient for excellent 
play. 

Chess, however, although decidable in principle by counting out all 
possible moves and responses, presents the problem inevitably connected 
with choice mazes: exponential growth. Alternative paths multiply so 
rapidly that we cannot even run through all the branching possibilities 
far enough to form a reliable judgment as to whether a given branch is 
sufficiently promising to merit further exploration. Newell notes that it 
would take much too long to find an interesting move if the machine had 
to examine the possible moves of each of the pieces on the board one after 
another. He is also aware that if this is not done, the machine may 
sometimes miss an important and original combination. "We do not 
want the machine to spend all its time examining the future actions of 
committed men; yet if it were never to do this, it could overlook real 
opportunities." 27 

NewelFs first solution was "the random element": "The machine 
should rarely [that is, occasionally] search for combinations which sac- 
rifice a Queen." 28 But this solution is unsatisfactory, as Newell himself, 
presumably, now realizes. The machine should not look just every once 
in a while for a Queen sacrifice but, rather, look in those situations in 
which such a sacrifice would be relevant. This is what the right heuristics 
are supposed to assure, by limiting the number of branches explored 
while retaining the more promising alternatives. 

But no master-level heuristics have as yet been found. All current 
heuristics either exclude some moves masters would find or leave open 
the risk of exponential growth. Simon is nonetheless convinced, for 
reasons to be discussed in Part II, that chess masters use such heuristics, 



What Computers Can't Do / 14 

and so he is confident that if we listen to their protocols, follow their eye 
movements, perhaps question them under bright lights, we can eventu- 
ally discover these heuristics and build them into our program thereby 
pruning the exponential tree. But let us examine more closely the evi- 
dence that chess playing is governed by the use of heuristics. 

Consider the following protocol quoted by Simon, noting especially 
how it begins rather than how it ends. The subject says, 

Again I notice that one of his pieces is not defended, the Rook, and there must 
be ways of taking advantage of this. Suppose now, if I push the pawn up at Bishop 
four, if the Bishop retreats I have a Queen check and I can pick up the Rook. 
If, etc., etc. 29 

At the end we have an example of what I shall call "counting out" 
thinking through the various possibilities by brute-force enumeration. 
We have all engaged in this process, which, guided by suitable heuristics, 
is supposed to account for the performance of chess masters. But how 
did our subject notice that the opponent's Rook was undefended? Did 
he examine each of his opponent's pieces and their possible defenders 
sequentially (or simultaneously) until he stumbled on the vulnerable 
Rook? That would use up too many considerations, for as Newell, Shaw, 
and Simon remark, "The best evidence suggests that a human player 
considers considerably less than 100 positions in the analysis of a 
move," 30 and our player must still consider many positions in evaluating 
the situation once the undefended Rook has been discovered. We need 
not appeal to introspection to discover what a player in fact does before 
he begins to count out; the protocol itself indicates it: the subject "zeroed 
in" on the promising situation ("I notice that one of his pieces is not 
defended"). Only after the player has zeroed in on an area does he begin 
to count out, to test, what he can do from there. 

An analysis of the MacHack program written by Richard Greenblatt 
will illustrate this difference between the way a human being sizes up a 
position and the machine's brute-force counting out. Even MacHack 
could not look at every alternative. The program contains a plausible 
move generator which limits the moves considered to the more prom- 
ising ones. Yet in a tough spot during a tournament, the Greenblatt 



Phase I (1957-1962) Cognitive Simulation / 75 

program once calculated for fifteen minutes and considered 26,000 
alternatives, while a human player can consider only 100, or possibly 
200, moves. MacHack came up with an excellent move, which is not to 
say a master could not have done even better; but what is significant 
here is not the quality of the move, but the difference between 26,000 
and 200 possibilities. This order of difference suggests that when play- 
ing chess, human beings are doing something different than just con- 
sidering alternatives, and the interesting question is: what are they 
doing that enables them, while considering 100 or 200 alternatives, to 
find more brilliant moves than the computer can find working through 
26,000? 

The human player whose protocol we are examining is not aware of 
having explicitly considered or explicitly excluded from consideration 
any of the hundreds of possibilities that would have had to have been 
enumerated in order to arrive at a particular relevant area of the board 
by counting out. Nonetheless, the specific portion of the board which 
finally attracts the subject's attention depends on the overall position. To 
understand how this is possible, we must consider what William James 
has called "the fringes of consciousness": the ticking of a clock which 
we notice only if it stops provides a simple example of this sort of 
marginal awareness. Our vague awareness of the faces in a crowd when 
we search for a friend is another, more complex and more nearly appro- 
priate, case. 

While suggesting an alternative to the explicit awareness of counting 
out, neither example is entirely appropriate, however. In neither of these 
cases does the subject make positive use of the information resting on the 
fringe. The chess case is best understood in terms of Michael Polanyi's 
general description of the power of the fringes of consciousness to con- 
centrate information concerning our peripheral experience. 

This power resides in the area which tends to function as a background because 
it extends indeterminately around the central object of our attention. Seen thus 
from the corner of our eyes, or remembered at the back of our mind, this area 
compellingly affects the way we see the object on which we are focusing. We may 
indeed go so far as to say that we are aware of this subsidiarily noticed area 
mainly in the appearance of the object to which we are attending. 31 * 



What Computers Can't Do / 16 

Once one is familiar with a house, for example, to him the front looks 
thicker than a fagade, because he is marginally aware of the house 
behind. Similarly, in chess, cues from all over the board, while remaining 
on the fringes of consciousness, draw attention to certain sectors by 
making them appear promising, dangerous, or simply worth looking 
into. 

As Newell and Simon themselves note: 

There are concepts in human chess playing that are much more global than those 
above; for example, a "developed position," "control of the center," "a won 
position,*' "a weak king side," "a closed position." 32 

Moreover, they admit that: 

Sometimes de Groot's subject used very global phrases such as "... and it's a 
won position for White," where it is not possible to see what structure or feature 
of the position leads to the evaluation. " 

This is Newell and Simon's way of saying that they see no way of 
analyzing this evaluation of the overall position in terms of heuristically 
guided counting out. And judiciously, but without seeming to realize 
what this does to the plausibility of Simon's predictions, Newell and 
Simon go on to note: 

To date the work on chess programs has not shed much new light on these 
higher-level concepts. 34 * 

The attitude of Newell and Simon is typically ambiguous here. Do 
they think that better static evaluators that is, better heuristics for 
generating plausible moves could simulate zeroing in? Their continued 
belief in the possibility of a mechanical chess master suggests they do. 
However, their analysis of master play, based on the work of de Groot, 
should be grounds for pessimism. (As we have seen, de Groot himself 
says he does not have much hope for substantial improvement of heuris- 
tic chess programs.) 

Newell and Simon note that 

De Groot finally succeeded in separating strong from weak players by using 
perceptual tests involving the reproduction of chess positions after brief exposure 
to them (3-7 seconds). The grandmaster was able to reproduce the positions 



Phase I (1957-1962) Cognitive Simulation / 17 

perfectly, and performance degraded appreciably with decrease in chess ability. 
De Groot was led to propose that perceptual abilities and organization were an 
important factor in very good play. 35 

In the article we have already discussed, chess master Hearst casts 
some further light on this perceptual process and why it defies program- 
ming: 

Apparently the master perceives the setup in large units, such as pawn structure 
of cooperating pieces. . . . When he does make an error, it is often one of putting 
a piece on a very desirable square for that type of position. 36 

Hearst sums up his view as follows: 

Because of the large number of prior associations which an experienced player 
has acquired, he does not visualize a chess position as a conglomeration of 
scattered squares and wooden pieces, but as an organized pattern (like the 
"Gestalt," or integrated configuration, emphasized by the Gestalt psycholo- 
gists.) 37 

Applying these ideas to our original protocol, we can conclude that 
our subject's familiarity with the overall chess pattern and with the past 
moves of this particular game enabled him to recognize the lines offeree, 
the loci of strength and weakness, as well as specific positions. He sees 
that his opponent looks vulnerable in a certain area (just as one familiar 
with houses in general and with a certain house sees it as having a certain 
sort of back), and zeroing in on this area he discovers the unprotected 
Rook. This move is seen as one step in a developing pattern. 

There is no chess program which even tries to use the past experience 
of a particular game in this way. Rather, each move is taken up anew 
as if it were an isolated chess problem found in a book. This technique 
is forced upon programmers, since a program which carried along infor- 
mation on the past position of each piece would rapidly sink under the 
accumulating data. What is needed is a program which selectively carries 
over from the past just those features which were significant in the light 
of its present strategy and the strategy attributed to its opponent. But 
since present programs embody no. long-range strategy at all, the only 
alternative would be to sort through all the stored information bit by bit 
which would be too time consuming. Without global awareness of overall 



What Computers Can't Do / 18 

patterns there seems to be no way of avoiding the problem of exponential 
growth or heuristic limitations on the possibilities which can be consid- 
ered. 38 * 

Since this global form of "information processing" in which informa- 
tion, rather than being explicitly considered remains on the fringes of 
consciousness and is implicitly taken into account, is constantly at work 
in organizing our experience, there is no reason to suppose that in order 
to discover an undefended Rook our subject must have counted out 
rapidly and unconsciously until he arrived at the area in which he began 
consciously counting out. Moreover, there are good reasons to reject this 
assumption, since it raises more problems than it solves. 

If the subject has been unconsciously counting out thousands of alter- 
natives with brilliant heuristics to get to the point where he focuses on 
that Rook, why doesn't he carry on with that unconscious process all the 
way to the end, until the best move just pops into his consciousness? 
Why, if the unconscious counting is rapid and accurate, does he resort 
to a cumbersome method of slowly, awkwardly, and consciously count- 
ing things out at the particular point where he spots the Rook? Or if, on 
the other hand, the unconscious counting is inadequate, what is the 
advantage of switching to a conscious version of the same process? 

This sort of teleological consideration while not a proof that uncon- 
scious processing is nondigital does put the burden of proof on those 
who claim that it is or must be. And those who make this claim have 
brought forward no arguments to support it. There is no evidence, 
behavioral or introspective, that counting out is the only kind of "infor- 
mation processing" involved in playing chess, that "the essential nature 
of the task [is] search in a space of exponentially growing possibilities." 39 
On the contrary, all protocols testify that chess involves two kinds of 
behavior: (1) zeroing in, by means of the overall organization of the 
perceptual field, on an area formerly on the fringes of consciousness, and 
which other areas still on the fringes of consciousness make interesting; 
and (2) counting out explicit alternatives. 

This distinction clarifies the early success and the later failure of work 
in cognitive simulation. In all game-playing programs, early success is 



Phase I (1957-1962) Cognitive Simulation / 19 

attained by working on those games or parts of games in which heuristi- 
cally guided counting out is feasible; failure occurs at the point where 
complexity is such that global awareness would be necessary to avoid an 
overwhelming exponential growth of possibilities to be counted. 

AMBIGUITY TOLERANCE VS, CONTEXT-FREE PRECISION 

Work on game playing revealed the necessity of processing "informa- 
tion" which is not explicitly considered or excluded, that is, information 
on the fringes of consciousness. Work in language translation has been 
halted by the need for a second nonprogrammable form of "information 
processing": the ability to deal with situations which are ambiguous 
without having to transform them by substituting a precise description. 

We have seen that Bar-Hillel and Oettinger, two of the most respected 
and best-informed workers in the field of automatic language translation, 
agree in their pessimistic conclusions concerning the possibility of fur- 
ther progress in the field. Each has realized that in order to translate a 
natural language, more is needed than a mechanical dictionary no 
matter how complete and the laws of grammar no matter how so- 
phisticated. The order of the words in a sentence does not provide 
enough information to enable a machine to determine which of several 
possible parsings is the appropriate one, nor do the surrounding words 
the written context always indicate which of several possible mean- 
ings is the one the author had in mind. 

As Oettinger says in discussing systems for producing all parsings of 
a sentence acceptable to a given grammar: 

The operation of such analyzers to date has revealed a far higher degree of 
legitimate syntactic ambiguity in English and in Russian than has been an- 
ticipated. This, and a related fuzziness of the boundary between the grammatical 
and the non-grammatical, raises serious questions about the possibility of effec- 
tive fully automatic manipulations of English or Russian for any purpose of 
translation or information retrieval. 40 

Instead of claiming, on the basis of his early partial success with a 
mechanical dictionary, and later, along with Kuno and others, on syntac- 



What Computers Can't Do / 20 

tic analyzers, that in spite of a few exceptions and difficulties, the mystery 
surrounding our understanding of language is beginning to dissolve, 
Oettinger draws attention to the "very mysterious semantic processes 
that enable most reasonable people to interpret most reasonable sen- 
tences unequivocally most of the time." 41 

Here is another example of the importance of fringe consciousness. 
Obviously, the user of a natural language is not aware of many of the 
cues to which he responds in determining the intended syntax and mean- 
ing. On the other hand, nothing indicates that he considers each of these 
cues unconsciously. In fact, two considerations suggest that these cues 
are not the sort that could be taken up and considered by a sequential 
or even a parallel program. 42 * 

First, there is Bar-HillePs argument, which we shall later study in 
detail (Chapter 6), that there is an infinity of possibly relevant cues. 
Second, even if a manageable number of relevant cues existed, they 
would not help us, for in order to program a computer to use such cues 
to determine the meaning of an utterance, we would have to formulate 
syntactic and semantic criteria in terms of strict rules; whereas our use 
of language, while precise, is not strictly rulelike. Pascal already noted 
that the perceptive mind functions "tacitly, naturally, and without tech- 
nical rules." Wittgenstein has spelled out this insight in the case of 
language. 

We are unable clearly to circumscribe the concepts we use; not because we don't 
know their real definition, but because there is no real "definition" to them. To 
suppose that there must be would be like supposing that whenever children play 
with a ball they play a game according to strict rules. 43 * 

A natural language is used by people involved in situations in which 
they are pursuing certain goals. These extralinguistic goals, which need 
not themselves be precisely stated or statable, provide some of the cues 
which reduce the ambiguity of expressions as much as is necessary for 
the task at hand. A phrase like "stay near me" can mean anything from 
"press up against me" to "stand one mile away," depending upon 
whether it is addressed to a child in a crowd or a fellow astronaut 
exploring the moon. Its meaning is never unambiguous in all possible 



Phase I (1957-1962) Cognitive Simulation / 21 

situations as if this ideal of exactitude even makes sense but the 
meaning can always be made sufficiently unambiguous in any particular 
situation so as to get the intended result. 

Our ability to use a global context to reduce ambiguity sufficiently 
without having to formalize (that is, eliminate ambiguity altogether) 
reveals a second fundamental form of human "information processing," 
which presupposes the first. Fringe consciousness takes account of cues 
in the context, and probably some possible parsings and meanings, all of 
which would have to be made explicit in the output of a machine. Our 
sense of the situation then allows us to exclude most of these possibilities 
without explicit consideration. We shall call the ability to narrow down 
the spectrum of possible meanings as much as the situation requires 
"ambiguity tolerance." 

Since a human being using and understanding a sentence in a natural 
language requires an implicit knowledge of the sentence's context- 
dependent use, the only way to make a computer that could understand 
and translate a natural language may well be, as Turing suspected, to 
program it to learn about the world. Bar-Hillel remarks: "I do not believe 
that machines whose programs do not enable them to learn, in a sophis- 
ticated sense of this word, will ever be able to consistently produce 
high-quality translations." 44 When occasionally artificial intelligence en- 
thusiasts admit the difficulties confronting present techniques, the appeal 
to learning is a favorite panacea. Seymour Papert of M.I.T., for example, 
has recently claimed that one cannot expect machines to perform like 
adults unless they are first taught, and that what is needed is a machine 
with the child's ability to learn. This move, however, as we shall see, only 
evades the problem. 

In the area of language learning, the only interesting and successful 
program is Feigenbaum's EPAM (Elementary Perceiver and Memo- 
rizer). EPAM simulates the learning of the association of nonsense sylla- 
bles, which Feigenbaum calls a simplified case of verbal learning. 45 The 
interesting thing about nonsense syllable learning, however, is that it is 
not a case of language learning at all. Learning to associate nonsense 
syllables is, in fact, acquiring something like a Pavlovian conditioned 



What Computers Can't Do / 22 

reflex. The experimenter could exhibit "DAX" then "JIR," or he could 
flash red and then green lights; as long as two such events were associated 
frequently enough, one would learn to anticipate the second member of 
the pair. In such an experiment, the subject is assumed to be completely 
passive. In a sense, he isn't really learning anything, but is having some- 
thing done to him. Whether the subject is an idiot, a child, or an adult 
should ideally make no difference in the case of nonsense syllable learn- 
ing. Ebbinghaus, at the end of the nineteenth century, proposed this form 
of conditioning precisely to eliminate any use of meaningful grouping or 
appeal to a context of previously learned associations. 

It is no surprise that subject protocol and machine trace most nearly 
match in this area. But it is a dubious triumph: the only successful case 
of cognitive simulation simulates a process which does not involve com- 
prehension, and so is not genuinely cognitive. 

What is involved in learning a language is much more complicated and 
more mysterious than the sort of conditioned reflex involved in learning 
to associate nonsense syllables. To teach someone the meaning of a new 
word, we can sometimes point at the object which the word names. 
Augustine, in his Confessions, and Turing, in his article on machine 
intelligence, assume that this is the way we teach language to children. 
But Wittgenstein points out that if we simply point at a table, for exam- 
ple, and say "brown," a child will not know if brown is the color, the 
size, or the shape of the table, the kind of object, or the proper name of 
the object. If the child already uses language, we can say that we are 
pointing out the color; but if he doesn't already use language, how do 
we ever get off the ground? Wittgenstein suggests that the child must be 
engaged in a "form of life" in which he shares at least some of the goals 
and interests of the teacher, so that the activity at hand helps to delimit 
the possible reference of the words used. 

What, then, can be taught to a machine? This is precisely what is in 
question in one of the few serious objections to work in artificial intelli- 
gence made by one of the workers himself. A. L. Samuel, who wrote the 
celebrated checkers program, has argued that machines cannot be intelli- 
gent because they can only do what they are instructed to do. Minsky 



Phase I (1957-1962) Cognitive Simulation / 23 

dismisses this objection with the remark that we can be surprised by the 
performance of our machines. 46 But Samuel certainly is aware of this, 
having been beaten by his own checkers program. He must mean some- 
thing else, presumably that the machine had to be given the program by 
which it could win, in a different sense than children are taught to play 
checkers. But if this is his defense, Samuel is already answered by Mi- 
chael Scriven. Scriven argues that new strategies are " 'put into* the 
computer by the designer ... in exactly the same metaphorical sense that 
we put into our children everything they come up with in their later 
life." 47 Still, Samuel should not let himself be bullied by the philosophers 
any more than by his colleagues. Data are indeed put into a machine but 
in an entirely different way than children are taught. We have just seen 
that when language is taught it is not, and, as we shall see in Chapter 
6, cannot be, precisely defined. Our attempts to teach meaning must be 
disambiguated and assimilated in terms of a shared context. Learning as 
opposed to memorization and repetition requires this sort of judgment. 
Wittgenstein takes up this question as follows: 

Can someone be a man's teacher in this? Certainly. From time to time he gives 
him the right tip. . . . This is what learning and teaching are like here. . . . What 
one acquires here is not a technique; one learns correct judgements. There are 
also rules, but they do not form a system, and only experienced people can apply 
them right. Unlike calculation rules. 48 * 

It is this ability to grasp the point in a particular context which is true 
learning; since children can and must make this leap, they can and do 
surprise us and come up with something genuinely new. 

The foregoing considerations concerning the essential role of context 
awareness and ambiguity tolerance in the use of a natural language 
should suggest why, after the success of the mechanical dictionary, 
progress has come to a halt in the translating field. Moreover, since, as 
we have seen, the ability to learn a language presupposes the same 
complex combination of the human forms of "information processing" 
needed to understand a language, it is hard to see how an appeal to 
learning can be used to bypass the problems this area must confront. 



What Computers Can't Do / 24 



ESSENTIAL/INESSENTIAL DISCRIMINATION VS. TRIAL-AND-ERROR 
SEARCH 

Work in problem solving also encounters two functions of thought: 
one, elementary and piecemeal, accounts for the early success in the field; 
another, more complex and requiring insight, has proved intractable to 
stepwise programs such as Simon's General Problem Solver. For simple 
problems it is possible to proceed by simply trying all possible combina- 
tions until one stumbles on the answer. This trial-and-error search is 
another example of a brute-force technique like counting out in chess. 
But, just as in game playing, the possibilities soon get out of hand. In 
problem solving one needs some systematic way to cut down the search 
maze so that one can spend one's time exploring promising alternatives. 
This is where people rely on insight and where programmers run into 
trouble. 

If a problem is set up in a simple, completely determinate way, with 
an end and a beginning and simple, specifically defined operations for 
getting from one to the other (in other words, if we have what Simon calls 
a "simple formal problem"), then Simon's General Problem Solver can, 
by trying many possibilities, bring the end and the beginning closer and 
closer together until the problem is solved. This would be a successful 
example of means-ends analysis. But even this simple case presents many 
difficulties. Comparing the machine print-out of the steps of a GPS 
solution with the transcript of the verbal report of a human being solving 
the same problem reveals steps in the machine trace (explicit searching) 
which do not appear in the subject's protocol. And Simon asks us to 
accept the methodologically dubious explanation of the missing steps in 
the human protocol that "many things concerning the task surely oc- 
curred without the subject's commenting on them (or being aware of 
them)" 49 and the even more arbitrary assumption that these further 
operations were of the same elementary sort as those verbalized. In fact, 
certain details of Newell and Simon's article, "GPS: A Program That 
Simulates Human Thought," suggest that these further operations are 
not like the programmed operations at all. 



Phase I (1957-1962) Cognitive Simulation 725 

In one of Simon's experiments, subjects were given problems in formal 
logic and a list of rules for transforming symbolic expressions and asked 
to verbalize their attempt to solve the problems. The details of the rules 
are not important; what is important is that at a point in the protocol 
the subject notes that he applies the rule (A B ^ A) and the rule 
(A B VB), to the conjunction ( -R v -P) - (R v Q). Newell and 
Simon comment: 

The subject handled both forms of rule 8 together, at least as far as his comment 
is concerned. GPS, on the other hand, took a separate cycle of consideration for 
each form. Possibly the subject followed the program covertly and simply re- 
ported the two results together. 50 

Possibly, however, the subject grasped the conjunction as symmetric 
with respect to the transformation operated by the rule, and so in fact 
applied both forms of the rule at once. Even Newell and Simon admit 
that they would have preferred that GPS apply both forms of the rule 
in the same cycle. Only then would their program provide a psychologi- 
cal theory of the steps the subject was going through. They wisely refrain, 
however, from trying to write a program which could discriminate be- 
tween occasions when it was appropriate to apply both forms of the rule 
at once and those occasions when it was not. Such a program, far from 
eliminating the above divergence, would require further processing not 
reported by the subject, thereby increasing the discrepancy between the 
program and the protocol. Unable thus to eliminate the divergence and 
unwilling to try to understand its significance, Newell and Simon dismiss 
the discrepancy as "an example of parallel processing." 51 * 

Another divergence noted by Newell and Simon, however, does not 
permit such an evasion. At a certain point, the protocol reads: "... I 
should have used rule 6 on the left-hand side of the equation. So use 6, 
but only on the left-hand side." Simon notes: 

Here we have a strong departure from the GPS trace. Both the subject and GPS 
found rule 6 as the appropriate one to change signs. At this point GPS simply 
applied the rule to the current expression; whereas the subject went back and 
corrected the previous application. Nothing exists in the program that corre- 



What Computers Can't Do / 26 

spends to this. The most direct explanation is that the application of rule 6 in 
the inverse direction is perceived by the subject as undoing the previous applica- 
tion of rule 6." 

This is indeed the most direct explanation, but Newell and Simon do 
not seem to realize that this departure from the trace, which cannot be 
explained away by parallel processing, is as detrimental to their theory 
as were the discrepancies in the movements of the planets to the 
Ptolemaic system. Some form of thinking other than searching is taking 
place! 

Newell and Simon note the problem: "It clearly implies a mechanism 
(maybe a whole set of them) that is not in GPS," 53 but, like the ancient 
astronomers, they try to save their theory by adding a few epicycles. 
They continue to suppose, without any evidence, that this mechanism is 
just a more elaborate search technique which can be accommodated by 
providing GPS with "a little continuous hindsight about its past ac- 
tions." 54 They do not realize that their assumption that intelligent behav- 
ior is always the result of following heuristic rules commits them to the 
implausible view that their subject's decision to backtrack must be the 
result of a very selective checking procedure. Otherwise, all past steps 
would have to be rechecked at each stage, which would hopelessly en- 
cumber the program. 

A more scientific approach would be to explore further the implica- 
tions of the five discrepancies noted in the article, in order to determine 
whether or not a different form of "information processing" might be 
involved. For example, Gestalt pyschologist Max Wertheimer points out 
in his classic work, Productive Thinking, that the trial-and-error account 
of problem solving excludes the most important aspect of problem- 
solving behavior, namely a grasp of the essential structure of the prob- 
lem, which he calls "insight." 55 In this operation, one breaks away from 
the surface structure and sees the basic problem what Wertheimer calls 
the "deeper structure" which enables one to organize the steps neces- 
sary for a solution. This gestaltist conception may seem antithetical to 
the operational concepts demanded by artificial intelligence, but Minsky 
recognizes the same need in different terms: 



Phase I (1957-1962) Cognitive Simulation / 27 

The ability to solve a difficult problem hinges on the ability to split or transform 
it into problems of a lower order of difficulty. To do this, without total reliance 
on luck, requires some understanding of the situation. One must be able to 
deduce, or guess, enough of the consequences of the problem statement to be able 
to set up simpler models of the problem situation. The models must have enough 
structure to make it likely that there will be a way to extend their solutions to 
the original problem. 56 

Since insight is necessary in solving complex problems and since what 
Minsky demands has never been programmed, we should not be sur- 
prised to find that in the work of Newell and Simon this insightful 
restructuring of the problem is surreptitiously introduced by the pro- 
grammers themselves. In The Processes of Creative Thinking, Newell, 
Shaw, and Simon introduce "the heuristics of planning" to account for 
characteristics of the subject's protocol lacking in a simple means-ends 
analysis. 

We have devised a program ... to describe the way some of our subjects handle 
O. K. Moore's logic problems, and perhaps the easiest way to show what is 
involved in planning is to describe that program. On a purely pragmatic basis, 
the twelve operators that are admitted in this system of logic can be put in two 
classes, which we shall call "essential" and "inessential" operators, respectively. 
Essential operators are those which, when applied to an expression, make "large" 
changes in its appearance change "P v P" to "P," for example. Inessential 
operators are those which make "small" changes e.g., change "P v Q" to 
"Q v P." As we have said, the distinction is purely pragmatic. Of the twelve 
operators in this calculus, we have classified eight as essential and four as inessen- 
tial. . . . 

Next, we can take an expression and abstract from it those features that relate 
only to essential changes. For example, we can abstract from "P v Q" the 
expression (PQ), where the order of the symbols in the latter expression is 
regarded as irrelevant. Clearly, if inessential operations are applied to the ab- 
stracted expressions, the expressions will remain unchanged, while essential 
operations can be expected to change them. . . . 

We can now set up a correspondence between our original expressions and 
operators, on the one hand, and the abstracted expressions and essential opera- 
tors, on the other. Corresponding to the original problem of transforming a into 
6, we can construct a new problem of transforming a' into b', where a' and b' 
are the expressions obtained by abstracting a and b respectively. Suppose that 
we solve the new problem, obtaining a sequence of expressions, a'c'd' . . . b'. 



What Computers Can't Do / 28 

We can now transform back to the original problem space and set up the new 
problems of transforming a into c, c into d, and so on. Thus, the solution of the 
problem in the planning space provides a plan for the solution of the original 
problem. 57 

No comment is necessary. One merely has to note that the actual pro- 
gram description begins in the second paragraph. The classification of 
the operators into essential and inessential, the function Wertheimer 
calls "finding the deeper structure" or "insight," is introduced by the 
programmers before the actual programming begins. 

This sleight of hand is overlooked by Miller, Galanter, and Pribram 
in Plans and the Structure of Behavior, a book which presents a psycho- 
logical theory influenced by Newell, Shaw, and Simon's work. Miller et 
al. begin by quoting Polya, who is fully aware of the necessary role 
insight plays in problem solving: 

In his popular text, How to Solve It, Polya distinguishes . . . phases in the heuristic 
process: 

First, we must understand the problem. We have to see clearly what the data 
are, what conditions are imposed, and what the unknown thing is that we are 
searching for. 

Second, we must devise a plan that will guide the solution and connect the 
data to the unknown. 58 

Miller et al. then minimize the importance of phase I, or rather simply 
decide not to worry about it. 

Obviously, the second of these is most critical. The first is what we have described 
in Chapter 12 as the construction of a clear Image of the situation in order to 
establish a test for the solution of the problem; it is indispensable, of course, but 
in the discussion of well-defined problems we assume that it has already been 
accomplished. 59 

Still the whole psychological theory of problem solving will not be 
worth much if there is no way to bring step one into the computer model. 
Therefore, it is no surprise that ten pages later, after adopting Simon's 
means-ends analysis, we find Miller et al. referring with relief to Simon's 
"planning method," 60 presumably the very paragraphs we have just dis- 
cussed: 



Phase I (1957-1 962) Cognitive Simulation / 29 

A second very general system of heuristics used by Newell, Shaw, and Simon 
consists in omitting certain details of the problem. This usually simplifies the task 
and the simplified problem may be solved by some familiar plan. The plan used 
to solve the simple problem is then used as the strategy for solving the original, 
complicated problem. In solving a problem in the propositional calculus, for 
example, the machine can decide to ignore differences among the logical connec- 
tives and the order of the symbols. . . . 6l 

But, as we have seen, it is not the machine that decides, but Newell, 
Shaw, and Simon, themselves. To speak of heuristics here is completely 
misleading, since no one has succeeded in formulating the rules which 
guide this preliminary choice or even in showing that at this stage, where 
insight is required, people follow rules. Thus we are left with no com- 
puter theory of the fundamental first step in all problem solving: the 
making of the essential/inessential distinction. Only those with faith 
such as that of Miller et al. could have missed the fact that Simon's 
"planning method," with its predigesting of the material, poses the prob- 
lem for computer simulation rather than provides the solution. 

This human ability to distinguish the essential from the inessential in 
a specific task accounts for the divergence of the protocol of the problem- 
solving subjects from the GPS trace. We have already suggested that the 
subject applies both forms of rule 8 together because he realizes at this 
initial stage that both sides of the conjunction are functionally equiva- 
lent. Likewise, because he has grasped the essential function of rule 6, 
the subject can see that a second application of the rule simply neutral- 
izes the previous one. As Wertheimer notes: 

The process [of structuring a problem] does not involve merely the given parts 
and their transformations. It works in conjunction with material that is structur- 
ally relevant but is selected from past experience. . . ." 

Since game playing is a form of problem solving we should expect to 
find the same process in chess playing, and indeed we do. To quote 
Hearst: 

De Groot concluded from his study that differences in playing strength depend 
much less on calculating power than on "skill in problem conception." Grand- 
masters seem to be superior to masters in isolating the most significant features 
of a position, rather than in the total number of moves that they consider. 



What Computers Can't Do / 30 

Somewhat surprisingly, de Groot found that grandmasters do not examine more 
possibilities on a single move than lower-ranked experts or masters (an average 
of two to four first moves per position) nor do they look further ahead (usually 
a maximum of six to seven moves ahead for each). The grandmaster is somehow 
able to "see** the core of the problem immediately, whereas the expert or lesser 
player finds it with difficulty, or misses it completely, even though he analyzes 
as many alternatives and looks as many moves ahead as the grandmaster. 63 

In 1961, as we have seen, Minsky was already aware of these problems. 
But his only hope was that one would discover a planning program 
which would use more of the same sort of heuristic searching on a higher 
level: 

When we call for the use of "reasoning," we intend no suggestion of giving up 
the game by invoking an intelligent subroutine. The program that administers 
the search will be just another heuristic program. Almost certainly it will be 
composed largely of the same sorts of objects and processes that will comprise 
the subject-domain programs. 64 

But such a planning program itself would require a distinction between 
essential and inessential operators. Unless at some stage the programmer 
himself introduces this distinction, he will be forced into an infinite 
regress of planning programs, each one of which will require a higher- 
order program to structure its ill-structured data. At this point, the 
transition from the easy to the difficult form of "information proces- 
sing," Minsky makes the typical move to learning. 

The problem of making useful deductions from a large body of statements (e.g. 
about the relevance of different methods to different kinds of problems) raises a 
new search problem. One must restrict the logical exploration to data likely to 
be relevant to the current problem. This selection function could hardly be 
completely built in at the start. It must develop along with other data ac- 
cumulated by experience. 65 

But thus far no one has even tried to suggest how a machine could 
perform this selection operation, or how it could be programmed to learn 
to perform it, since it is one of the conditions for learning from past 
experience. 

Feigenbaum, in a recent appraisal of work done since Computers and 
Thought, notes the glaring lack of learning programs: 



Phase I (1957-1962) Cognitive Simulation / 31 

The AI field still has little grasp of the machine learning problem for problem 
solvers. For many years, almost the only citation worth making was to Samuel's 
famed checker playing program and its learning system. (Great interest arose 
once in a scheme proposed by Newell, Shaw, and Simon for learning in GPS, 
but the scheme was never realized.) Surprisingly, today we face the same situa- 
tion. 66 

This lack of progress is surprising only to those, like Feigenbaum, who 
do not recognize the ability to distinguish the essential from the inessen- 
tial as a human form of "information processing," necessary for learning 
and problem solving, yet not amenable to the mechanical search tech- 
niques which may operate once this distinction has been made. It is 
precisely this function of intelligence which resists further progress in the 
problem-solving field. 

It is an illusion, moreover, to think that the planning problem can be 
solved in isolation; that essential/inessential operations are given like 
blocks and one need only sort them out. It is easy to be hypnotized by 
oversimplified and ad hoc cases like the logic problem into thinking 
that some operations are essential or inessential in themselves. It then 
looks as if we can find them because they are already there, so that we 
simply have to discover a heuristic rule to sort them out. But normally 
(and often even in logic) essential operations are not around to be found 
because they do not exist independently of the pragmatic context. 

In the light of their frank inevitable recourse to the insightful predi- 
gesting of their material, there seems to be no foundation for Newell, 
Shaw, and Simon's claim that the behavior vaguely labeled cleverness or 
keen insight in human problem solving is really just the result of the 
judicious application of certain heuristics for narrowing the search for 
solutions. Their work with GPS, on the contrary, demonstrates that all 
searching, unless directed by a preliminary structuring of the problem, 
is merely muddling through. 

Ironically, research in Cognitive Simulation is a perfect example of 
so-called intelligent behavior which proceeds like the unaided GPS. Here 
one finds the kind of tinkering and ad hoc patchwork characteristic of 
a fascination with the surface structure a sort of tree-climbing with 
one's eyes on the moon. Perhaps it is just because the field provides no 



What Computers Can't Do / 32 

example of insight that some people in Cognitive Simulation have mis- 
taken the operation of GPS for intelligent behavior. 

PERSPICUOUS GROUPING VS. CHARACTER LISTS 

A computer must .recognize all patterns in terms of a list of specific 
traits. This raises problems of exponential growth which human beings 
are able to avoid by proceeding in a different way. Simulating recognition 
of even simple patterns may thus require recourse to each of the funda- 
mental forms of human "information processing" discussed this far. And 
even if in these simple cases artificial intelligence workers have been able 
to make some headway with mechanical techniques, patterns as complex 
as artistic styles and the human face reveal a loose sort of resemblance 
which seems to require a special combination of insight, fringe conscious- 
ness, and ambiguity tolerance beyond the reach of digital machines. It 
is no wonder, then, that work in pattern recognition has had a late start 
and an early stagnation. 

In Chapter 1 we noted that a weakness of current pattern recognition 
programs (with the possible exception of the Uhr-Vossler program, the 
power of whose operators since it only, recognizes five letters has not 
yet been sufficiently tested) is that they are not able to determine their 
own selection operators. Now, however, we shall see that this way of 
presenting the problem is based on assumptions that hide deeper and 
more difficult issues. 

Insight A first indication that human pattern recognition differs radi- 
cally from mechanical recognition is seen in human (and animal) toler- 
ance for changes in orientation and size, degrees of incompleteness and 
distortion, and amount of background noise. 

An early artificial intelligence approach was to try to normalize the 
pattern and then to test it against a set of templates to see which it 
matched. Human recognition, on the other hand, seems to simply disre- 
gard changes in size and orientation, as well as breaks in the figure, and 
so on. Although certain perceptual constants do achieve some normali- 
zation (apparent size and brightness do not vary as much as correspond- 
ing changes in the signal reaching the retina), it seems clear that we do 



Phase I (1957-1962) Cognitive Simulation / 33 

not need to fully normalize and smooth out the pattern, since we can 
perceive the pattern as skewed, incomplete, large or small, and so on, at 
the same time we recognize it. 

More recent programs, rather than normalizing the pattern, seek pow- 
erful operators which pick out discriminating traits but remain insensi- 
tive to distortion and noise. But human beings, when recognizing 
patterns, do not seem to employ these artificial expedients either. In 
those special cases where human beings can articulate their cues, these 
turn out not to be powerful operators which include sloppy patterns and 
exclude noise, but rather a set of ideal traits which are only approximated 
in the specific instances of patterns recognized. Distorted patterns are 
recognized not as falling under some looser and more ingenious set of 
traits, but as exhibiting the same simple traits as the undistorted figures, 
along with certain accidental additions or omissions. Similarly, noise is 
not tested and excluded; it is ignored as inessential. 67 * Here again, we 
note the human ability to distinguish the essential from the inessential. 

Fringe Consciousness. To determine which of a set of already analyzed 
patterns a presented pattern most nearly resembles, workers have pro- 
posed analyzing the presented pattern for a set of traits by means of a 
decision tree; or by combining the probabilities that each of a set of traits 
is present, as in Selfridge's Pandaemonium program. Either method 
uncritically assumes that a human being, like a mechanical pattern 
recognizer, must classify a pattern in terms of a specific list of traits. It 
seems self-evident to Selfridge and Neisser that "a man who abstracts a 
pattern from a complex of stimuli has essentially classified the possible 
inputs." 68 Earl Hunt makes the same assumption in his review of pattern 
recognition work: "Pattern recognition, like concept learning, involves 
the learning of a classification rule." 69 

Yet, if the pattern is at all complicated and sufficiently similar to many 
other patterns so that many traits are needed for discrimination, the 
problem of exponential growth threatens. Supposing that a trait-by-trait 
analysis is the way any pattern recognizer, human or artificial, must 
proceed thus leads to the assumption that there must be certain crucial 
traits if one could only find them, or program the machine to find them 
for itself which would make the processing manageable. 



What Computers Can't Do / 34 

One is led to look for a sort of perceptual heuristic, the "powerful 
operators" which no one as yet has been able to find. And just as the 
chess masters are not able to provide the programmer with the heuristic 
shortcuts they are supposed to be using, Selfridge and Neisser note in the 
case of pattern recognition that "very often the basis of classification is 
unknown, even to [the analyzer]." Nevertheless, Selfridge and Neisser 
assume, like Newell and Simon, that unconsciously a maze is being 
explored in this case, that a list of traits is being searched. They are thus 
led to conclude that "it [the basis of classification] is too complex to be 
specified explicitly." 70 

But the difficulties involved in searching such a list suggest again that, 
for human beings at least, not all possibly relevant traits are taken up in 
a series or in parallel and used to make some sort of decision, but that 
many traits crucial to discrimination are never taken up explicitly at all 
but do their work while remaining on the fringe of consciousness. 

Whereas in chess we begin with a global sense of the situation and have 
recourse to counting out only in the last analysis, in perception we need 
never appeal to any explicit traits. We normally recognize an object as 
similar to other objects without being aware of it as an example of a type 
or as a member of a class defined in terms of specific traits. As Aron 
Gurwitsch puts it in his analysis of the difference between perceptual and 
conceptual consciousness: 

Perceived objects appear to us with generic determinations. . . . But and this 
is the decisive point to perceive an object of a certain kind is not at all the same 
thing as grasping that object as representative or as a particular case of a type. 71 

Of course, we can sometimes make the defining traits explicit: 

The first step in the constituting of conceptual consciousness consists in effecting 
a dissociation within the object perceived in its typicality. The generic traits 
which until then were immanent and inherent in the perceived thing are detached 
and disengaged from it. Rendered explicit, these traits can be seized in them- 
selves. . . . Consequent upon this dissociation, the generic becomes the general. 
From this aspect it opposes itself to the thing perceived from which it has just 
been disengaged, and which now is transformed into an example, a particular 
instance. . . . 

[Thus, cues] can be grasped and become themes [specific traits we are aware 



Phase I (1957-1962) Cognitive Simulation 735 

of] . . . , whereas previously they only contributed to the constitution of another 
theme [the pattern] within which they played only a mute role. 72 

This shift from perceptual to conceptual consciousness (from the per- 
ceptive to the mathematical frame of mind, to use Pascal's expression), 
is not necessarily an improvement. Certain victims of aphasia, studied 
by Gelb and Goldstein, have lost their capacity for perceptual recogni- 
tion. All recognition for the patient becomes a question of classification. 
The patient has to resort to checklists and search procedures, like a 
digital computer. Some such aphasics can only recognize a figure such 
as a triangle by listing its traits, that is, by counting its sides and then 
thinking: "A triangle has three sides. Therefore, this is a triangle." 73 Such 
conceptual recognition is time consuming and unwieldy; the victims of 
such brain injuries are utterly incapable of getting along in the everyday 
world. 

Evidently, in pattern recognition, passing from implicit perceptual 
grouping to explicit conceptual classification even at some final stage, 
as in chess is usually disadvantageous. The fact that we need not con- 
ceptualize or thematize the traits common to several instances of the 
same pattern in order to recognize that pattern distinguishes human 
recognition from machine recognition, which only occurs on the explicit 
conceptual level of class membership. 

Context-Dependent Ambiguity Reduction. In the cases thus far consid- 
ered, the traits defining a member of a class, while generally too numer- 
ous to be useful in practical recognition, could at least in principle always 
be made explicit. In some cases, however, such explicitation is not even 
possible. To appreciate this point we must first get over the idea, shared 
by traditional philosophers and workers in artificial intelligence alike, 
that pattern recognition can always be understood as a sort of classifica- 
tion. In this overhasty generalization three distinct kinds of pattern 
recognition are lumped together, none of which has the characteristics 
philosophers and digital computers demand. 

First there is the recognition of what Gurwitsch calls the generic. An 
example of such recognition would be the recognition of a certain object 
as a pencil. As Gurwitsch has pointed out, this form of recognition, while 



What Computers Can't Do / 36 

not explicit, lends itself to explicitation in terms of a set of features. It 
might thus seem adapted to being programmed. But what Gurwitsch 
overlooks in his account is that in this form of recognition our purposes 
serve to select which features are significant, and, among these, certain 
features which are crucial. For example, it is significant for our purposes 
that a pen have a point. However, when a writing instrument with a ball 
at the end was introduced, the end was nonetheless called a point (not 
a tip), and the instrument a ball-point pen (not a pencil), presumably 
because it was crucial to the users that the mark this instrument made 
could not be erased. 

We might conclude that making an indelible mark is a defining crite- 
rion for being a pen, whereas having a point is only what Wittgenstein 
calls a symptom ". . . a phenomenon of which the experience has taught 
us that it coincided, in some way or other, with the phenomenon which 
is our defining criterion." We might even try to introduce this distinction 
between symptom and criterion into our program. But Wittgenstein's 
essential point in distinguishing between symptom and criterion is that 
the distinction is not fixed once and for all but changes with our changing 
purposes and knowledge: 

In practice, if you were asked which phenomenon is the defining criterion and 
which is a symptom, you would in most cases be unable to answer this question 
except by making an arbitrary decision ad hoc. It may be practical to define a 
word by taking one phenomenon as the defining criterion, but we shall easily be 
persuaded to define the word by means of what, according to our first use, was 
a symptom. Doctors will use names of diseases without ever deciding which 
phenomena are to be taken as criteria and which as symptoms; and this need not 
be a deplorable lack of clarity. 74 

Indeed, it is one way our concepts gain the openness crucial to human 
pattern recognition, a flexibility lacking in a computer using a fixed set 
of essential features. 

A second sort of pattern recognition is the recognition of resemblance. 
In this sort of recognition, as in "narrowing down" 75 * the meaning of 
words or sentences, the context plays a determining role. The context 
may simply lead us to notice those resemblances which we can subse- 
quently recognize in isolation as in the case of ambiguous figures such 



Phase I (1957-1962) Cognitive Simulation / 37 

as Wittgenstein's duck-rabbit, which resembles a duck when surrounded 
by pictures of ducks and a rabbit when surrounded by rabbits or it may 
lead us to focus on certain aspects of the pattern, as in Pudovkin's famous 
experiment: 

One day Pudovkin took a close-up of Mosjoukin with a completely impassive 
expression and projected it after showing: first, a bowl of soup, then, a young 
woman lying dead in her coffin, and last, a child playing with a teddy-bear. The 
first thing noticed was that Mosjoukin seemed to be looking at the bowl, the 
young woman, and the child, and next one noted that he was looking pensively 
at the dish, that he wore an expression of sorrow when looking at the woman, 
and that he had a glowing smile for the child. The audience was amazed at his 
variety of expression, although the same shot had actually been used all three 
times and was, if anything, remarkably inexpressive. 76 

Here, in a striking way, the meaning of the context determines what 
expression is seen on the face in a situation in which no traits of the face 
as projected on the screen could account for these differences. Still one 
might say that the expressive face, the one that the viewers thought they 
saw, had certain traits, like sad eyes, or a happy smile, which led the 
viewer to recognize the expression. But the expression of a person's eyes, 
for example, may depend on the whole face in such a way as to be 
unrecognizable if viewed through a slit. Moreover, a certain expression 
of the eyes may bring out a certain curve of the nose which would not 
be noticed if the nose were in another face; the nose in turn may give a 
certain twist to the smile which may affect the appearance of the eyes. 
As Wittgenstein remarks: "A human mouth smiles only in a human 
face." 77 In such cases, the traits necessary for recognizing a resemblance 
(dancing eyes, mocking smile, etc.) cannot, even when thematized, be 
isolated and defined in a neutral, context-free way. Moreover, as in the 
case of linguistic disambiguation, the context in this example the whole 
face not only determines the features essential for recognition, but is 
reciprocally determined by them. The expression is not deduced from the 
traits; it is simply the organization of the eyes, the mouth, and so forth, 
just as a melody is made up of the very notes to which it gives their 
particular values. In this sort of resemblance, the notion of recognizing 
the pattern in terms of isolated traits makes no sense. 



What Computers Can't Do / 38 

In another case of resemblance, objects recognized as belonging to- 
gether need not have any traits in common at all not even context- 
dependent ones. Wittgenstein, in his study of natural language, was led 
to investigate this type of nonclassifactory recognition: 

We see a complicated network of similarities overlapping and criss-crossing: 
Sometimes overall similarities, sometimes similarities of detail. 

I can think of no better expression to characterize these similarities than 
"family resemblances"; for the various resemblances between members of a 
family: build, features, color of eyes, gait, temperament, etc. etc., overlap and 
criss-cross in the same way. . . . We extend our concept . . as in spinning a thread 
we twist fiber on fiber. 78 

Family resemblance differs from class membership in several impor- 
tant ways: classes can be defined in terms of traits even if they have no 
members, whereas family resemblances are recognized only in terms of 
real or imaginary examples. 79 * Moreover, whereas class membership is 
all or nothing, 80 * family resemblance allows a spectrum ranging from the 
typical to the atypical. An atypical member of a family, for example, may 
be recognized by being placed in a series of faces leading from a typical 
member to the atypical one. Similarly, certain concepts like graceful, 
garish, and crude can not be defined in terms of necessary and sufficient 
conditions, but only by exhibiting a typical case. Since this sort of recog- 
nition of a member of a "family" is accomplished not by a list of traits, 
but by seeing the case in question in terms of its proximity to a paradigm 
(i.e., typical) case, such recognition gives us another kind of openness 
and flexibility. 

Finally Wittgenstein goes even further and suggests that in some kinds 
of recognition there may be no common traits, even overlapping ones. 
Wittgenstein continues the above remarks rather obscurely: 

. . . If someone wishes to say: "There is something common to all these construc- 
tions namely the disjunction of all their common properties" I should reply: 
Now you are only playing with words. One might as well say: "Something runs 
through the whole thread namely the continuous overlapping of these fibres." 81 

Wittgenstein here may be suggesting a third kind of recognition which 
he does not clearly distinguish from resemblance, but which we might 
call the recognition of similarity. 



Phase I (1957-1962) Cognitive Simulation / 39 

Wittgenstein, on this interpretation, should not be taken to mean that 
recognition involves so many overlapping traits, but that one cannot use 
such an unwieldy disjunction. A more consistent way of understanding 
his analysis would be to conclude that each of the traits he mentions in 
discussing family resemblance the build, color of eyes, gait, etc. is not 
identical in any two members of the family, but in turn consists of a 
network of crisscrossing similarities. To follow the analogy, each fiber is 
made of fibers all the way down. Thus, no two members of a family need 
have any identical features for them all to share a family resemblance. 
Similarity is the ultimate notion in Wittgenstein's analysis and it cannot 
be reduced as machine-thinking would require to a list or disjunction 
of identical, determinate features. 82 

Those capable of recognizing a member of a "family" need not be able 
to list any exactly similar traits common to even two members, nor is 
there any reason to suppose such traits exist. Indeed, formalizing family 
resemblance in terms of exactly similar traits would eliminate a kind of 
openness to new cases which is the most striking feature of this form of 
recognition. No matter what disjunctive list of traits is constructed, one 
can always invent a new "family" member whose traits are similar to 
those of the given members without being exactly similar to any of the 
traits of any of them, and which in some situation would be recognized 
as belonging with the others. 

This sophisticated but nonetheless very common form of recognition 
employs a special combination of the three forms of "information pro- 
cessing" discussed thus far: fringe consciousness, insight, and context 
dependence. To begin with, the process is implicit. It uses information 
which, in a manner of speaking, remains on the fringes of consciousness. 
To see the role of insight we must first distinguish the generic from the 
typical, although Gurwitsch uses these two terms interchangeably. As 
Gurwitsch defines it, recognition of the generic depends on implicit cues 
which can always be made explicit. Recognition of the typical, as we have 
been using the term, depends on similarities which cannot be thematized. 
Recognition of the typical, then, unlike recognition of the generic, re- 
quires insightful ordering around a paradigm. A paradigm case serves its 
function insofar as it is the clearest manifestation of what (essentially) 



What Computers Can't Do / 40 

makes all members, members of a given group. Finally, recognition in 
terms of proximity to the paradigm is a form of context dependence. 

Wittgenstein remarks that "a perspicuous representation produces just 
that understanding which consists in seeing connections." 83 Following 
Wittgenstein, we have called this combination of fringe consciousness, 
insight, and context determination "perspicuous grouping." This form 
of human "information processing" is as important as the three funda- 
mental forms of information processing from which it is derived. 

Summary. Human beings are able to recognize patterns under the 
following increasingly difficult conditions: 

1. The pattern may be skewed, incomplete, deformed, and embedded in 
noise; 

2. The traits required for recognition may be "so fine and so numerous" that, 
even if they could be formalized, a search through a branching list of such 
traits would soon become unmanageable as new patterns for discrimina- 
tion were added; 

3. The traits may depend upon external and internal context and are thus not 
amenable to context-free specification; 

4. There may be no common traits but a "complicated network of overlap- 
ping similarities," capable of assimilating ever new variations. 

Any system which can equal human performance, must therefore, be 
able to 

1. Distinguish the essential from the inessential features of a particular in- 
stance of a pattern; 

2. Use cues which remain on the fringes of consciousness; 

3. Take account of the context; 

4. Perceive the individual as typical, i.e., situate the individual with respect 
to a paradigm case. 

Since the recognition of patterns of even moderate complexity may 
require these four forms of human "information processing," work in 
pattern recognition has not progressed beyond the laborious recognition 
of simple alphanumeric patterns such as typewriter fonts and zip code 
figures. Moreover, it is generally acknowledged that further progress in 
game playing, language translation, and problem solving awaits a break- 
through in pattern recognition research. 



Phase I (1957-1962) Cognitive Simulation / 41 



Conclusion 

The basic problem facing workers attempting to use computers in the 
simulation of human intelligent behavior should now be clear: all alter- 
natives must be made explicit. In game playing, the exponential growth 
of the tree of these alternative paths requires a restriction on the paths 
which can be followed out; in complicated games such as chess, pro- 
grams cannot now select the most promising paths. In problem solving, 
the issue is not only how to direct a selective search among the explicit 
alternatives, but how to structure the problem so as to begin the search 
process. In language translation, even the elements to be manipulated are 
not clear due to the intrinsic ambiguities of a natural language; in pattern 
recognition, all three difficulties are inextricably intertwined, as well as 
the fact that similarity and typicality seem to be irreducible characteris- 
tics of perception. These difficulties have brought to a standstill the first 
five years of work on Cognitive Simulation. 

None of Simon's predictions has been fulfilled. The failure to fulfill the 
first two, about how well machines could do in chess and mathematics, 
gave the lie to Simon's third prediction concerning a psychological the- 
ory of human behavior. In spite of the eagerness and gullibility of psy- 
chologists, within the past ten years most theories in psychology have not 
taken the form of computer programs. 

Instead of these triumphs, an overall pattern has emerged: success 
with simple mechanical forms of information processing, great expecta- 
tions, and then failure when confronted with more complicated forms of 
behavior. Simon's predictions fall into place as just another example of 
the phenomenon which Bar-Hillel has called the "fallacy of the success- 
ful first step." 84 * Simon himself, however, has drawn no such sobering 
conclusions. In his latest prediction, made in 1965, Simon now affirms 
that "machines will be capable, within twenty years, of doing any work 
that a man can do." 85 

We shall devote Part II to the reasons for this imperturbable optimism, 
but first we must consider the work in AI which has taken up where work 
in Cognitive Simulation gave out. 



Phase II (1962-1967) Semantic Information 
Processing 



To place Phase I in perspective and to form an idea of what was 
expected and accomplished in Phase II, it is helpful to begin by quot- 
ing Minsky's brief account of the history of work on machine intelli- 
gence: 

In the early 1950's, as general-purpose computers became available to the scien- 
tific community, Cybernetics divided . . . into three chief avenues: The first was 
the continuation of the search for simple basic principles. This became trans- 
formed into the goal of discovering what we might call minimal, Self-Organizing 
Systems. A paradigm of this approach is to find large collections of generally 
similar components that, when arranged in a very weakly specified structure and 
placed in an appropriate environment, would eventually come to behave in an 
"adaptive" fashion. Eventually, it was hoped, intelligent behavior would emerge 
from the evolution of such a system. 1 

Since those still pursuing this course, sometimes called cybernetics, 
have produced no interesting results although their spokesman, Frank 
Rosenblatt, has produced some of the most fantastic promises and 
claims 2 * they will not be dealt with here. 

The second important avenue was an attempt to build working models of hu- 
man behavior, . . . requiring the machine's behavior to match that of human sub- 
jects 3 



Phase II (1962-1967) Semantic Information Processing / 43 

The book, Computers and Thought, edited by E. Feigenbaum and J. Feldman 
who did their graduate work in the Carnegie group, gives a good view of the state 
of affairs as it stood by about the end of 1961. 4 

This is the research in Cognitive Simulation, led by Newell and Simon, 
which we have criticized in Chapter 1. Minsky is similarly critical of this 
work in a paper delivered at the time Phase I was nearing its end: 

Methods that worked quite well on easy problems did not extend smoothly to 
the difficult ones. Continued progress will require implementation of new ideas, 
for there are some very tough problems in our immediate path. 5 

This is Minsky's way of recognizing the stagnation we have noted. At 
the same time Minsky and his group at M.I.T. undertook to provide new 
ideas and their implementation: 

The third approach, the one we call Artificial Intelligence, was an attempt to 
build intelligent machines without any prejudice toward making the system 
simple, biological, or humanoid. Workers taking this route regarded the self- 
organizing systems as unpromising or, perhaps, premature. Even if simplicity of 
initial organization was to be an ultimate goal, one might first need experience 
with working intelligent systems (based if necessary on ad hoc mechanisms) if 
one were eventually to be able to design more economical schemes. 6 

We shall now turn to this third and most recent approach, the results 
of which are reported in Minsky's book Semantic Information Process- 
ing, to see just what has actually been accomplished. Minsky once sug- 
gested that in evaluating the programs presented in his book one might 
ask five questions: 

1. Why were these particular problems selected? 

2. How do these programs work? 

3. What are their limitations? 

4. What do the programs actually achieve? 

5. How can they be extended to larger domains of competence? 

If, following this method, we analyze the programs which Minsky pre- 
sents as the best work since 1962, we shall find that unlike work done 
before 1961, which tended to give the impression of intelligence by 
simulating simple, mechanical aspects of intelligent behavior, the current 
approach is characterized by ad hoc solutions of cleverly chosen problems, 



What Computers Can't Do / 44 

which give the illusion of complex intellectual activity. In fact, however, 
problems which arrested work in 1961 still remain unsolved. We shall 
also find again that only an unquestioned underlying faith enables work- 
ers such as Minsky to find this situation encouraging. 
Let us look at the programs in detail. 

I. Analysis of Semantic Information Processing 
Programs 

ANALYSIS OF A PROGRAM WHICH "UNDERSTANDS ENGLISH" 
BOBROW'S STUDENT 

Of the five semantic information processing programs collected in 
Minsky's book, Daniel Bobrow's STUDENT a program for solving 
algebra word problems is put forward as the most successful. It is, 
Minsky tells us, "a demonstration par excellence of the power of using 
meaning to solve linguistic problems." 7 Indeed, Minsky devotes a great 
deal of his Scientific American article to Bobrow's program and goes so 
far as to say that "it understands English." 8 

Since this program is presented as the best so far, we shall begin 
by analyzing it in detail, according to Minsky's suggested five ques- 
tions. 

First: Why was this particular problem selected? 

Bobrow himself tells us: 

In constructing a question-answering system many problems are greatly sim- 
plified if the problem context is restricted. 9 

Moreover, 

There are a number of reasons for choosing the context of algebra story problems 
in which to develop techniques which would allow a computer problem solving 
system to accept a natural language input. First, we know a good type of data 
structure in which to store information needed to answer questions in this 
context, namely, algebraic equations. 10 

It is important to note that the problem was chosen because the 
restricted context made it easier. The full significance of this restriction, 



Phase I! (1962-1967) Semantic Information Processing / 45 

however, will only be evident after we have answered the next two 
questions. 

How does the program work? 

The program simply breaks up the sentences of the story problem into 
units on the basis of cues such as the words "times," "of," "equals," etc.; 
equates these sentence chunks with x's and /s; and tries to set up 
simultaneous equations. If these equations cannot be solved, it appeals 
to further rules for breaking up the sentences into other units and tries 
again. The whole scheme works only because there is the constraint, not 
present in understanding ordinary discourse, that the pieces of the sen- 
tence, when represented by variables, will set up soluble equations. As 
Minsky puts it: ". . . some possibly syntactic ambiguities in the input are 
decided on the overall basis of algebraic consistency. . . ." H 

Choosing algebra problems also has another advantage: 

In natural language, the ambiguities arise not only from the variety of structural 
groupings the words could be given, but also from the variety of meanings that 
can be assigned to each individual word. In STUDENT the strong semantic 
constraint (that the sentences express algebraic relations between the designated 
entities) keeps the situation more or less under control. 12 

What are the limitations of the program? 

The advantage of using algebraic constraints is also a serious limita- 
tion on the generality of the program, however, for such a "strong 
constraint" eliminates just that aspect of natural language, namely its 
ambiguity, which makes machine processing of natural language diffi- 
cult, if not impossible. Such a program is so far from semantic under- 
standing that, as Bobrow admits, ". . . the phrase 'the number of times 
I went to the movies' which should be interpreted as a variable string will 
be interpreted incorrectly as the product of the two variables 'number 
of and 'I went to the movies,' because 'times' is always considered to 
be an operator." 13 

What, then, has been achieved? 

Bobrow is rather cautious. Although his thesis is somewhat mislead- 
ingly entitled "Natural Language Input for a Computer Problem Solving 



What Computers Can't Do / 46 

Program," Bobrow makes clear from the outset that the program "ac- 
cepts as input a comfortable but restricted subset of English. " M He 
adds: 

In the following discussion, I shall use phrases such as "the computer under- 
stands English." In all such cases, the "English" is just the restricted subset of 
English allowable as input for the computer program under discussion. 15 

This is straightforward enough, and seems an admirable attempt to claim 
no more than is justified by the restricted choice of material. In the 
course of the work, Bobrow even makes clear that "The STUDENT 
program considers words as symbols, and makes do with as little knowl- 
edge about the meaning of words as is compatible with the goal of finding 
a solution of the particular problem." 16 

In other words this program embodies a minimum of semantic under- 
standing. Bobrow is proud that he can get so much for so little: "The 
semantic model in the STUDENT system is based on one relationship 
(equality) and five basic arithmetic functions." 17 

Bobrow is equally careful in noting he has given a special meaning to 
"understands." 

For purposes of this report I have adopted the following operational definition 
of "understanding." A computer understands a subset of English if it accepts 
input sentences which are members of this subset, and answers questions based 
on information contained in the input. The STUDENT system understands 
English in this sense. 18 * 

Bobrow concludes cautiously: "I think we are far from writing a pro- 
gram which can understand all, or even a very large segment, of English. 
However, within its narrow field of competence, STUDENT has demon- 
strated that 'understanding' machines can be built." 19 

Yet Minsky says in his Scientific American article that "STUDENT 
. . . understands English." What has happened? 

Bobrow's quotation marks around "understanding" are the key. If we 
remember that "understands" merely means "answers questions in a 
restricted subset of English subject to algebraic constraints," then we will 
also remember that although the words in quotation marks have nothing 



Phase II (1962-1967) Semantic Information Processing / 47 

to do with what human understanding normally means, they are 
nonetheless accurate. However, one can't help being misled into feeling 
that if Bobrow uses "understands" rather than "processes," it must be 
because his program has something to do with human understanding. 
Minsky exploits this ambiguity in his rhetorical article simply by drop- 
ping the quotation marks. 

Minsky makes even more surprising and misleading claims concerning 
the "enormous 'learning potential' " of Bobrow's program: 

Consider the qualitative effect, upon the subsequent performance of Bobrow's 
STUDENT, of telling it that "distance equals speed times timer That one 
experience alone enables it to handle a large new portion of "high-school alge- 
bra"; the physical position-velocity-time problems. It is important not to fall into 
the habit ... of concentrating only on the kind of "learning" that appears as 
slow-improvement-attendant-upon-sickeningly-often-repeated experience! 

Bobrow's program does not have any cautious statistical devices that have 
to be told something over and over again, so its learning is too brilliant to be 
called so. 20 

Again it is easy to show that what has been acquired by the machine 
can in no way be called "understanding." The machine has indeed been 
given another equation, but it does not understand it as a formula. That 
is, the program can now plug one distance, one rate, and one time into 
the equation d = rt; but that it does not understand anything is clear 
from the fact that it cannot use this equation twice in one problem, 
for it has no way of determining which quantities should be used in 
which equation. As Bobrow admits: "the same phrase must always be 
used to represent the same variable in a problem." 21 No learning has 
occurred. 

Once he has removed the quotation marks from "understand" and 
interpreted the quotation marks around "learning" to mean superhuman 
learning, Minsky is free to engage in the usual riot of speculation. 

In order for a program to improve itself substantially it would have to have at 
least a rudimentary understanding of its own problem-solving process and some 
ability to recognize an improvement when it found one. There is no inherent 
reason why this should be impossible for a machine. Given a model of its own 



What Computers Can't Do / 48 

workings, it could use its problem-solving power to work on the problem of 
self-improvement. . . . 

Once we have devised programs with a genuine capacity for self-improvement 
a rapid evolutionary process will begin. As the machine improves both itself and 
its model of itself, we shall begin to see all the phenomena associated with the 
terms "consciousness," "intuition" and "intelligence" itself. It is hard to say how 
close we are to this threshold, but once it is crossed the world will not be the 
same. 22 

It is not as hard to say how close we are to this threshold as Minsky 
would like us to believe. Since the success of Bobrow's program has 
allegedly given us the rudiments of understanding and learning that 
Minsky is relying on, we need only ask: to what extent can Bobrow's 
techniques be generalized and extended? 

Which leads us to question five: How can the program in question be 
extended to larger domains of competence? 

Here even Bobrow throws his caution to the winds and in spite of 
his earlier remark that the semantic model is based on one relationship 
(equality); that is, only sets up and solves equations where it can use the 
algebraic constraint claims that his "semantic theory of discourse can 
be used as a basis for a much more general language processing sys- 
tem." 23 And Bobrow concludes the abstract of his thesis with the now 
familiar first-step fallacy: "The STUDENT system is a first step toward 
natural language communication with computers. Further work on the 
semantic theory proposed should result in much more sophisticated 
systems." 24 

Five years have passed since Bobrow made this claim, and no more 
sophisticated semantic theory has been forthcoming. Why Bobrow and 
Minsky think, in the face of the peculiar restrictions necessary to the 
function of the program, that such a generalization must be possible is 
hard to understand. Nothing, I think, can justify or even explain their 
optimism concerning this admittedly limited and ad hoc approach. Their 
general optimism that some such computable approach must work, how- 
ever, can be seen to follow from a fundamental metaphysical assumption 
concerning the nature of language and of human intelligent behavior, 
namely that whatever orderly behavior people engage in can in principle 



Phase II (1962-1967) Semantic Information Processing / 49 

be formalized and processed by digital computers. (See Chapter 5.) This 
leads Minsky and Bobrow to shrug off all current difficulties as techno- 
logical limitations, imposed, for example, by the restricted size of the 
storage capacity of present machine memories. 25 * 

Were it not for such an assumption, Bobrow's limited success, her- 
alded by Minsky as the most promising work thus far, would be recog- 
nized as a trick which says nothing either for or against the possibility 
of machine understanding, and the fact that this is the best that an 
intelligent person like Bobrow could do would lead to discouragement 
as to the possibility of ever reaching the threshold of self-improving 
machines. 

EVANS' ANALOGY PROGRAM 

The same pattern occurs throughout Minsky's collection: an ad hoc 
solution of a restricted problem, first reported with caution, and then 
interpreted as being the first step to more general methods. Yet all the 
work presented in Minsky's book was completed by 1964, and although 
seven more years have elapsed, none of the promised generalizations has 
been produced. 

Evans* analogy-finding program, for example, is a masterful complex 
program for solving the sort of analogy problems used in intelligence 
testing. (See Figure 2.) It performs its particular restricted task as well 
as an average tenth grader, which, granted the state of the art, is an 
impressive performance. Evans, moreover, realizes that this success as 
such has little value unless the techniques he employs can be generalized. 
But, unlike Bobrow, he does not content himself with the assertion that 
such a generalization is possible. Rather, he attempts at the end of his 
paper to sketch the form such a generalization would take, and the 
contribution it would make to problem-solving programs such as GPS 
and work in pattern recognition. 

In the final pages of this chapter we describe a "pattern recognition" process of 
which the main outlines are based on the conception of ANALOGY described. 
It is more ambitious chiefly in that a more powerful and more general-purpose 
descriptive framework for the "objects" is introduced. 26 



What Computers Can't Do 



750 



A is to a as C is to ? 




Figure 2 

GPS treats sub-objects of a given object through its goal-subgoal organization. 
That is, GPS avoids looking at complex structures on a given level by decompos- 
ing them into smaller structures tied to subgoals. So GPS never sees a single 
complex structure as such; when a substructure is handled at some deeper 
subgoal level it is "out of context" in that the necessary information as to how 
the achievement of this subgoal contributes to the achievement of larger goals 
is lacking. Newell discusses a form of this "lack of context" problem and several 
rather unsatisfactory attempts at solving it. The mechanism we have sketched 
provides a pattern-recognition device capable of taking a look at the problem 
which is "global" yet has access to the full structure. Such "global" guidance 
could be expected to save GPS a large amount of the time now spent in setting 
up and pursuing subgoals that do not contribute to achieving goals at or near 
the top level. This alone would be a worthwhile contribution. 27 

Evans also has proposals for learning: 

Certainly the study of these problems in the relatively well-understood domain 
of phrase-structure languages is a natural next step toward the development of 
genuine "generalization learning" by machines and a prerequisite to considera- 
tion of learning in still more complex descriptive language environments. One 
interesting possibility, since the transformation rules themselves can be described 
in phrase-structure terms, would be to apply the entire "phrase-structure + 
GPS" apparatus to improving its own set of transformation rules. 28 

Evans realizes that "this may, of course, turn out to be very difficult. 1 ' 29 
Presumably it has so turned out, because no more has been published 
concerning this scheme since this work was completed in 1963, and, as 
we have seen, since then Newell has abandoned GPS and Murray Eden 



Phase II (1962-1967) Semantic Information Processing / 57 

has reported that in 1968 pattern recognition was as ad hoc as ever. 
Which, of course, raises the usual question: Why do Minsky and Evans 
so confidently expect that the ad hoc techniques used to solve this specific 
and rather complex analogy problem can be generalized? A hint as to 
the assumptions underlying this confidence can be found in Minsky's 
surprising comparison of Evans' program to human analogy solving. In 
spite of his disclaimers that AI is not interested in cognitive simula- 
tion, Minsky gives the following "mentalistic" description of Evans' pro- 
gram. 

To explain the spirit of this work, it is best to describe the program in mentalistic 
form. Given a set of figures, it constructs a set of hypotheses or theories as 
follows: 

1. Based on the descriptions D(A) and D(B) of Figures A and B [see Figure 
2] there are many ways in which D(A) can be transformed into D(B); 
choose one of these. 

2. There are also many ways in which the parts of A can be put into corre- 
spondence with the parts of C: each such correspondence suggests a rela- 
tion like that proposed in (1), but which now relates Fig. C and some other 
figures. 

3. It is unlikely that any of the relations found in (2) will apply perfectly to 
any of the answer-figures. (If just one does, then that will be the program's 
answer.) For each answer figure, "weaken," i.e., generalize each relation 
just enough so that it will apply to the figure. 

4. Finally, the program measures how much it had to weaken each relation. 
It chooses the one that required the least change, and gives the correspond- 
ing answer figure as its answer. 

By choosing that hypothesis which involved the least "weakening" of the 
original A >-B transformation hypothesis, the program selects that explanation 
that contains the most information common to both A >* B and C VD 
relations. The details of the selection rules in steps (1), (2), (3), and (4), amount, 
in effect to Evans 9 theory of human behavior in such situations. I feel sure 
that something of this general character is involved in any kind of analogical rea- 
soning. 30 

This "something" is put more clearly in Minsky's Scientific American 
article. There he says: "I feel sure that rules or procedures of the same 
general character are involved in any kind of analogical reasoning." 31 
This is the same assumption which, as we have seen, underlies Newell 



What Computers Can't Do / 52 

and Simon's work in CS. In fact, Evans uses a quotation from Newell 
to describe the problem-solving procedure involved: 

"These programs are all rather similar in nature. For each the task is difficult 
enough to allow us to assert that the programs problem-solve, rather than simply 
carry out the steps of a procedure for a solution invented by the programmer. 
They all operate on formalized tasks, which, although difficult, are not unstruc- 
tured. All the programs use the same conceptual approach: they interpret the 
problem as combinatorial, involving the discovery of the right sequence of opera- 
tions out of a set of possible sequences. All of the programs generate some sort 
of tree of possibilities to gradually explore the possible sequences. The set of all 
sequences is much too large to generate and examine in toto, so that various 
devices, called heuristics, are employed to narrow the range of possibilities to a 
set that can be handled within the available limits of processing effort." 

Evans then concludes: 

The geometric-analogy program also fits this description. Stated very briefly, 
given a problem of this type, the program uses various heuristics to select a 
"correct" rule (in a reasonable time) from a very extensive class of possible 
rules." 

It is true that if human beings did solve analogy problems in this way, 
there would be every reason to expect to be able to improve and general- 
ize Evans' program, since human beings certainly surpass the machines' 
present level of performance. But, as in the case of GPS, there is no 
evidence that human beings proceed in this way, and descriptive, psycho- 
logical evidence suggests that they do not. 

Rudolph Arnheim, professor of psychology at Harvard University, in 
discussing Evans' work, has described the different way in which human 
beings approach the same sort of problem. His description is worth 
quoting in full: 

What happens when a person is confronted with a figure such as Figure [2]? The 
reaction will vary somewhat from individual to individual as long as no particu- 
lar context calls for concentration on specific structural features. By and large, 
however, the observer is likely to notice a vertical arrangement, made up of two 
units, of which the upper is larger and more complex than the lower; he may also 
notice a difference in shape. In other words, he will perceive qualitative charac- 
teristics of placement, relative size, shape; whereas he is unlikely to notice much 



Phase II (1962-1967) Semantic Information Processing / 53 

of the metric properties from which the computer's reading of the pattern must 
set out, namely, absolute size and the various lengths and distances by which this 
individual figure is constructed. If one asks observers to copy such a figure, their 
drawings will show concentration on the topological characteristics and neglect 
of specific measurements. 

Confronted now with a pairing of A and B, the human observer may have a 
rather rich and dazzling experience. He may see, at first, fleeting, elusive resem- 
blances among basically different patterns. The over-all figure, made up of the 
pairing of the two, may look unstable, ungraspable, irrational. There are two 
vertical arrangements, combining in a sort of symmetry; but these two columns 
are crossed and interfered with by diagonal relations between the two "filled" 
large circles and the two smaller, unfilled shapes. The various structural features 
do not add up to a unified, stable, understandable whole. Suddenly, however, 
the observer may be struck by the simple rectangular arrangement of the four 
smaller figures: two equal circles on top, two equal squares at the bottom. As 
soon as this group becomes the dominant theme or structural skeleton of the 
whole, the remainder the two large circles joins the basic pattern as a sec- 
ondary, diagonal embellishment. A structural hierarchy has been established. 
Now the double figure is stable, surveyable, understandable, and therefore ready 
for comparison with other figures. A first act of problem solving has taken 
place. 

If the observer turns to Figure C, his view of this new pattern is determined 
from the outset by his preceding concern with A and B. Perceived from the 
viewpoint of A, C reveals a similar vertical structure, distinguished from A 
mainly by a secondary contrast of shapes. The family resemblance is great, the 
relation comes easily. But if C is now paired with D t , the resemblance looks 
excessive, the symmetry too complete. On the contrary, a comparison with D 2 
offers too little resemblance. D 3 is recognized immediately as the correct partner, 
the missing fourth element of the analogy, if the relation between A and B had 
been properly grasped before. 

This episode of perceptual problem solving has all the aspects of genuine 
thinking: the challenge, the productive confusion, the promising leads, the partial 
solutions, the disturbing contradictions, the flash appearance of a stable solution 
whose adequacy is self-evident, the structural changes brought about by the 
pressure of changing total situations, the resemblance discovered among different 
patterns. It is, in a small way, an exhilarating experience, worthy of a creature 
endowed with reason; and when the solution has been found, there is a sense of 
dis-tension, of pleasure, of rest. 

None of this is true for the computer, not because it is without consciousness, 
but because it proceeds in a fundamentally different fashion. We are shocked to 
learn that in order to make the machine solve the analogy problem the experi- 



What Computers Can't Do / 54 

menter "had to develop what is certainly one of the most complex programs 
ever written." For us the problem is not hard; it is accessible to the brain of a 
young student. The reason for the difference is that the task calls for the han- 
dling of topological relations, which require the neglect of purely metric ones. 
The brain is geared to precisely such topographical features because they inform 
us of the typical character of things, rather than of their particular measure- 
ments. 33 

As in the case of chess, it turns out that global perceptual grouping 
is a prior condition for the rule-governed counting out the only kind 
of procedure available to the machine. As Arnheim puts it, "Topology 
was discovered by, and relies on, the perceptual powers of the brain, not 
the arithmetical ones." 34 

Obviously Minsky and Evans think that analogies are solved by hu- 
man beings by applying transformation rules, because the prospects for 
AI are only encouraging if this is how humans proceed. But it is clearly 
circular to base one's optimism on an hypothesis which, in turn, is only 
justified by the fact that if the hypothesis were true, one's optimism 
would be justified. 

QUILLIAN'S SEMANTIC MEMORY PROGRAM 

The final program we shall consider from Phase II, Ross Quillian's 
Semantic Memory Program, is the most interesting, because most gen- 
eral; and the most modest, in that its author (working under Simon 
rather than Minsky) has made no sweeping promises or claims. 35 * This 
program confirms a general evaluation heuristic already apparent in 
Samuel's modesty and success and Simon's and Gelernter's claims and 
setbacks, namely that the value of a program is often inversely propor- 
tional to its programmer's promises and publicity. 

Quillian, like Bobrow, is interested in simulating the understanding of 
natural language; but, unlike Bobrow and Minsky, he sees that this 
problem cannot be dealt with by ad hoc solutions. 

In the first place, we do not believe that performance theories or computer 
models can ignore or put off semantics, as most language processing programs 



Phase II (1962-1967) Semantic Information Processing / 55 

so far have done, and yet hope to achieve success. Whether a program is intended 
to parse sentences, to translate languages, or to answer natural language ques- 
tions, if it does not take account of semantic facts both early and often, I do not 
think it has a chance of approaching the level of human competence. 36 

After reviewing all work in the field, including that of Bobrow, Quil- 
lian remarks: 

Programs such as Bobrow's have been able to set up the equations corresponding 
to certain algebra word problems by an almost entirely "syntactic" procedure. 
. . . However, if one attempts to extend the range of language that such a program 
can handle, it becomes necessary to incorporate increasing numbers of semantic 
facts. 37 

Quillian concludes that 

the problems of what is to be contained in an overall, human-like permanent 
memory, what format this is to be in, and how this memory is to be organized 
have not been dealt with in great generality in prior simulation programs. 
. . . Further advances in simulating problem-solving and game playing, as well 
as language performance, will surely require programs that develop and interact 
with large memories. 38 

Quillian then proceeds to propose a complex heuristic program for 
storing and accessing the meaning of words and "anything that can be 
stated in language, sensed in perception, or otherwise known and remem- 
bered" 39 in one "enormous interlinked net." 40 Quillian proposes this 
program as "a reasonable view of how semantic information is organized 
within a person's memory." 41 He gives no argument to show that it is 
reasonable except that if a computer were to store semantic information, 
this would be a reasonable model for it. People, indeed, are not aware 
of going through any of the complex storage and retrieval process Quil- 
lian outlines, but this does not disturb Quillian, who, like his teacher, 
Simon, in similar trouble can always claim that these processes are 
nonetheless unconsciously taking place: 

While the encoding process is of course not identical to the covert processing that 
constitutes the understanding of the same text during normal reading, it is 
... in some ways a slowed-down, overt version of it. 42 



What Computers Can't Do / 56 

That such unconscious processing is going on, and moreover, that 
such processing follows heuristic rules is by no means obvious. We have 
seen in the cases of chess playing and analogy solving that gestalt group- 
ing plays a crucial role, and it may well do so here. Yet Quillian seems 
to have inherited Newell and Simon's unquestioned assumption that 
human beings operate by heuristic programs. 

The heuristic methods by which one particular comprehension of text is selected 
is the central problem for anyone who would explain "understanding," just as 
the heuristic methods by which one particular chess move is selected from 
all those possible is the central problem for anyone who would explain chess 
playing. 43 

In terms of this assumption Quillian must assume that the task of the 
program involves working from parts to wholes. 

In selecting a task to perform with a model memory, one thinks first of the ability 
to understand unfamiliar sentences. It seems reasonable to suppose that people 
must necessarily understand new sentences by retrieving stored information 
about the meaning of isolated words and phrases, and then combining and 
perhaps altering these retrieved word meanings to build up the meanings of 
sentences. Accordingly, one should be able to take a model of stored semantic 
knowledge, and formulate rules of combination that would describe how sen- 
tence meanings get built up from stored word meanings. 44 

Quillian also has great hopes for his system: 

It further seems likely that if one could manage to get even a few word meanings 
adequately encoded and stored in a computer memory, and a workable set of 
combination rules formalized as a computer program, he could then bootstrap 
his store of encoded word meanings by having the computer itself "understand" 
sentences that he had written to constitute the definitions of other single words. 
That is, whenever a new, as yet uncoded, word could be defined by a sentence 
using only words whose meanings had already been encoded, then the represen- 
tation of this sentence's meaning, which the machine could build by using 
its previous knowledge together with its combination rules, would be the ap- 
propriate representation to add to its memory as the meaning of the new 
word. 43 

But with a frankness, rare in the literature, Quillian also reports his 
disappointments: 



Phase II (1962-1967) Semantic Information Processing / 57 

Unfortunately, two years of work on this problem led to the conclusion that the 
task is much too difficult to execute at our present stage of knowledge. The 
processing that goes on in a person's head when he "understands" a sentence and 
incorporates its meaning into his memory is very large indeed, practically all of 
it being done without his conscious knowledge. 46 

The magnitude of the problem confronting Quillian becomes clear 
when we note that 

the definition of eight hundred and fifty words comprise far more information 
than can be modeled in the core of today's computers. . . . 47 

These difficulties suggest that the model itself the idea that our un- 
derstanding of a natural language involves building up a structured 
whole out of an enormous number of explicit parts may well be mis- 
taken. Quillian's work raises rather than resolves the question of storing 
the gigantic number of facts resulting from an analysis which has no 
place for perceptual gestalts. If this data structure grows too rapidly with 
the addition of new definitions, then Quillian's work, far from being 
encouraging, would be a reductio ad absurdum of the whole computer- 
oriented approach. Before taking a stand on whether Quillian's work is 
grounds for optimism, one would expect an answer to the basic question: 
Does Quillian's data base grow linearly or exponentially with additional 
entries? 

On this crucial point it is surprising to find much hope but little 
information. Quillian's program contains definitions of only from 50 to 
60 words, and, in describing Quillian's work, in his book written in 1968, 
three years after the work was completed, Minsky has to admit that "we 
simply do not know enough about how powerful Quillian's methods 
would be when provided with a more substantial knowledge bank." 48 
Again, no further progress has been reported. 

II. Significance of Current Difficulties 

What would be reasonable to expect? Minsky estimates that Quillian's 
program now contains a few hundred facts. He estimates that "a million 
facts would be necessary for great intelligence." 49 He also admits that 



What Computers Can't Do / 58 

each of "the programs described [in this book] will work best when given 
exactly the necessary facts, and will bog down inexorably as the informa- 
tion files grow." 50 

Is there, thus, any reason to be confident that these programs are 
approaching the "superior heuristics for managing their knowledge 
structure" which Minsky believed human beings must have; or, as 
Minsky claims in another of his books, that 

within a generation . . . few compartments of intellect will remain outside the 
machine's realm the problem of creating "artificial intelligence" will be sub- 
stantially solved. 51 

Certainly there is nothing in Semantic Information Processing to justify 
this confidence. As we have seen, Minsky criticizes the early programs 
for their lack of generality. "Each program worked only on its restricted 
specialty, and there was no way to combine two different problem- 
solvers." 52 But Minsky's solutions are as ad hoc as ever. Yet he adds 
jauntily: 

The programs described in this volume may still have this character, but they 
are no longer ignoring the problem. In fact, their chief concern is finding methods 
of solving it. 53 

But there is no sign that any of the papers presented by Minsky have 
solved anything. They have not discovered any general feature of the 
human ability to behave intelligently. All Minsky presents are clever 
special solutions, like Bobrow's and Evans', or radically simplified mod- 
els such as Quillian's, which work because the real problem, the problem 
of how to structure and store the mass of data required has been put 
aside. Minsky, of course, has already responded to this apparent short- 
coming with a new version of the first step fallacy: 

The fact that the present batch of programs still appear to have narrow ranges 
of application does not indicate lack of progress toward generality. These pro- 
grams are steps toward ways to handle knowledge. 54 

In Phase II the game seems to be to see how far one can get with the 
appearance of complexity before the real problem of complexity has to 



Phase II (1962-1967) Semantic Information Processing / 59 

be faced, and then when one fails to generalize, claim to have made a first 
step. 

Such an approach is inevitable as long as workers in the field of AI 
are interested in producing striking results but have not solved the 
practical problem of how to store and access the large body of data 
necessary, if perhaps not sufficient, for full-scale, flexible, semantic infor- 
mation processing. Minsky notes with satisfaction, looking over the 
results, "one cannot help being astonished at how far they did get with 
their feeble semantic endowment." 55 Bar-Hillel in a recent talk to SI- 
GART (Special Interest Group in Artificial Intelligence of the Associa- 
tion for Computing Machinery) calls attention to the misleading 
character of this sort of claim. 

There are very many people in all fields but particularly in the field of AI 
who, whenever they themselves make a first step towards letting the computer 
do certain things it has never done before, then believe that the remaining steps 
are nothing more than an exercise in technical ability. Essentially, this is like 
saying that as soon as anything can be done at all by a computer, it can also be 
done well. On the contrary, the step from not being able to do something at 
all to being able to do it a little bit is very much smaller than the next step 
being able to do it well. In AI, this fallacious thinking seems to be all perva- 
sive. 56 

But Bar-Hillel is too generous in suggesting that the fallacy is sim- 
ply overestimation of the ease of progress. To claim to have taken even 
an easy first step one must have reason to believe that by further such 
steps one could eventually reach one's goal. We have seen that Min- 
sky's book provides no such reasons. In fact these steps may well be 
strides in the opposite direction. The restricted character of the results 
reported by Minsky, plus the fact that during the last five years none 
of the promised generalizations has been produced, suggests that 
human beings do not deal with a mass of isolated facts as does a digital 
computer, and thus do not have to store and retrieve these facts by 
heuristic rules. Judging from their behavior, human beings avoid rather 
than resolve the difficulties confronting workers in Cognitive Simu- 
lation and Artificial Intelligence by avoiding the discrete informa- 



What Computers Can't Do / 60 

tion-processing techniques from which these difficulties arise. Thus it 
is by no means obvious that Minsky's progress toward handling 
"knowledge" (slight as it is) is progress toward artificial intelligence 
at all. 



Conclusion 



We have seen how Phase I, heralded as a first step, ends with the 
abandonment of GPS and the general failure to provide the theorem 
proving, chess playing, and language translation programs anticipated. 
Minsky himself recognizes this failure, and while trying to minimize it, 
diagnoses it accurately: 

A few projects have not progressed nearly as much as was hoped, notably 
projects in language translation and mathematical theorem-proving. Both cases, 
I think, represent premature attempts to handle complex formalisms without 
also representing their meaning. 1 

Phase II a new first step begins around 1961 with Minsky 's gradu- 
ate students at M.I.T. undertaking theses aimed at overcoming this 
difficulty. It ends in 1968 with the publication of Minsky's book Semantic 
Information Processing, which reports these attempts, all completed by 
1964. After analyzing the admittedly ad hoc character of those programs 
which Minsky considers most successful, and noting the lack of follow- 
up during the last five years, we can only conclude that Phase II has also 
ended in failure. 

Most reports on the state of the art try to cover up this failure. In a 
report undertaken for the IEEE in 1966, covering work in AI since 1960, 
R. J. Solomonoff devotes his first three pages to speaking of GPS and 
other past achievements, already completed by 1960, and the next three 



What Computers Can't Do / 62 

pages to talking of the glorious future of work on induction by S. Amarel: 
"Although Amarel hasn't programmed any of his theories, his ideas and 
his analysis of them are important." 2 There is little mention of the 
semantic information processing programs touted by Minsky. All hope 
is placed in induction and learning. Unfortunately, "in all the learning 
systems mentioned, the kinds of self improvement accessible to the ma- 
chines have been quite limited. . . . We still need to know the kind of 
heuristics we need to find heuristics, as well as what languages can 
readily describe them." 3 

Since no one has made any contribution to finding these heuristics, 
Solomonoff s final hope is placed in artificial evolution: 

The promise of artificial evolution is that many things are known or suspected 
about the mechanisms of natural evolution, and that those mechanisms can be 
used directly or indirectly to solve problems in their artificial counterparts. For 
artificial intelligence research, simulation of evolution is incomparably more 
promising than simulation of neural nets, since we know practically nothing 
about natural neural nets that would be at all useful in solving difficult problems. 4 

This work in artificial evolution, however, has hardly begun. "Research 
in simulation of evolution has been very limited in both quantity and 
quality." 5 

When an article supposed to sum up work done since 1960 begins with 
earlier accomplishments and ends in speculations, without presenting a 
single example of actual progress, stagnation can be read between the 
lines. 

Occasionally one catches hints of disappointment in the lines them- 
selves. For example, Fred Tonge, whose solid, unpretentious paper on 
a heuristic line-balancing procedure was reprinted in Computers and 
Thought, after reviewing progress in AI, concluded in 1968: 

While many interesting programs (and some interesting devices) have been pro- 
duced, progress in artificial intelligence has not been exciting or spectacular. 
. . . This is due at least in part to lack of a clear separation between accomplish- 
ment and conjecture in many past and current writings. In this field, as in many 
others, there is a large difference between saying that some accomplishment 
"ought to" be possible and doing it. 
Identifiable, significant, landmarks of accomplishment are scarce. 6 



Conclusion / 63 

Tonge then gives his list of "landmarks." They are Newell, Shaw, and 
Simon's Logic Theory Machine, Samuel's Checker Program, and the 
Uhr-Vossler pattern recognition program all completed long before 
1961, and all dead ends if we are to judge from subsequent work. 

That mine is no unduly prejudiced reaction to Tonge's summary of the 
work done thus far can be seen by comparing P. E. Greenwood's review 
of Tonge's article for Computing Reviews: "From this brief summary of 
the state of the art of artificial intelligence, one would conclude that little 
significant progress has been made since about 1960 and the prospects 
for the near future are not bright." 7 

Why, in the light of these difficulties, do those pursuing Cognitive 
Simulation assume that the information processes of a computer reveal 
the hidden information processes of a human being, and why do those 
working in Artificial Intelligence assume that there must be a digital way 
of performing human tasks? To my knowledge, no one in the field seems 
to have asked himself these questions. In fact, artificial intelligence is the 
least self-critical field on the scientific scene. There must be a reason why 
these intelligent men almost unanimously mimimize or fail to recognize 
their difficulties, and continue dogmatically to assert their faith in prog- 
ress. Some force in their assumptions, clearly not their success, must 
allow them to ignore the need for justification. We must now try to 
discover why, in the face of increasing difficulties, workers in artificial 
intelligence show such untroubled confidence. 



PARTI 



ASSUMPTIONS UNDERLYING 



PERSISTENT OPTIMISM 



Introduction 



In spite of grave difficulties, workers in Cognitive Simulation and 
Artificial Intelligence are not discouraged. In fact, they are unqualifiedly 
optimistic. Underlying their optimism is the conviction that human 
information processing must proceed by discrete steps like those of a 
digital computer, and, since nature has produced intelligent behavior 
with this form of processing, proper programming should be able to elicit 
such behavior from digital machines, either by imitating nature or by 
out-programming her. 

This assumption, that human and mechanical information processing 
ultimately involves the same elementary processes, is sometimes made 
naTvely explicit. Newell and Simon introduce one of their papers with the 
following remark: 

It can be seen that this approach makes no assumption that the "hardware** of 
computers and brains are similar, beyond the assumptions that both are general- 
purpose symbol-manipulating devices, and that the computer can be pro- 
grammed to execute elementary information processes functionally quite like 
those executed by the brain. 1 

But this is no- innocent and empty assumption. What is a general- 
purpose symbol-manipulating device? What are these "elementary infor- 
mation processes" allegedly shared by man and machine? All artificial 
intelligence work is done on digital computers because they are the only 



What Computers Can't Do / 68 

general-purpose information-processing devices which we know how to 
design or even conceive of at present. All information with which these 
computers operate must be represented in terms of discrete elements. In 
the case of present computers the information is represented by binary 
digits, that is, in terms of a series of yeses and noes, of switches being 
open or closed. The machine must operate on finite strings of these 
determinate elements as a series of objects related to each other only by 
rules. Thus the assumption that man functions like a general-purpose 
symbol-manipulating device amounts to 

1. A biological assumption that on some level of operation usually 
supposed to be that of the neurons the brain processes information in 
discrete operations by way of some biological equivalent of on/off 
switches. 

2. A psychological assumption that the mind can be viewed as a 
device operating on bits of information according to formal rules. Thus, 
in psychology, the computer serves as a model of the mind as conceived 
of by empiricists such as Hume (with the bits as atomic impressions) and 
idealists such as Kant (with the program providing the rules). Both 
empiricists and idealists have prepared the ground for this model of 
thinking as data processing a third-person process in which the in- 
volvement of the "processor" plays no essential role. 

3. An epistemological assumption that all knowledge can be formal- 
ized, that is, that whatever can be understood can be expressed in terms 
of logical relations, more exactly in terms of Boolean functions, the 
logical calculus which governs the way the bits are related according to 
rules. 

4. Finally, since all information fed into digital computers must be in 
bits, the computer model of the mind presupposes that all relevant 
information about the world, everything essential to the production of 
intelligent behavior, must in principle be analyzable as a set of situation- 
free determinate elements. This is the ontological assumption that what 
there is, is a set of facts each logically independent of all the others. 

In the following chapters we shall turn to an analysis of the plausibility 
of each of these assumptions. In each case we shall see that the assump- 



Introduction / 69 

tion is taken by workers in CS or AI as an axiom, guaranteeing results, 
whereas it is, in fact, only one possible hypothesis among others, to be 
tested by the success of such work. Furthermore, none of the four 
assumptions is justified on the basis of the empirical and a priori argu- 
ments brought forward in its favor. Finally, the last three assumptions, 
which are philosophical rather than empirical, can be criticized on phil- 
osophical grounds. They each lead to conceptual difficulties when fol- 
lowed through consistently as an account of intelligent behavior. 

After we have examined each of these assumptions we shall be in a 
better position to understand the persistent optimism of workers in 
artificial intelligence and also to assess the true significance of results 
obtained thus far. 



The Biological Assumption 



In the period between the invention of the telephone relay and its 
apotheosis in the digital computer, the brain, always understood in terms 
of the latest technological inventions, was understood as a large tele- 
phone switchboard or, more recently, as an electronic computer. This 
model of the brain was correlated with work in neurophysiology which 
found that neurons fired a somewhat all-or-nothing burst of electricity. 
This burst, or spike, was taken to be the unit of information in the brain 
corresponding to the bit of information in a computer. This model is still 
uncritically accepted by practically everyone not directly involved with 
work in neurophysiology, and underlies the naive assumption that man 
is a walking example of -a successful digital- computer program. 

But to begin with, even if the brain did function like a digital computer 
at some level it would not necessarily provide encouragement for those 
working in CS or AI. For the brain might be wired like a very large array 
of randomly connected neurons, such as the perceptrons proposed by the 
group Minsky dismisses as the early cyberneticists. 1 * Such a neural net 
can be simulated using a program, but such a program is in no sense a 
heuristic program. Thus the mere fact that the brain might be a digital 
computer is in no way ground for optimism as to the success of artificial 
intelligence as defined by Simon or Minsky. 

Moreover, it is an empirical question whether the elementary informa- 



What Computers Can't Do / 72 

tion processing in the brain can best be understood in terms of a digital 
model. The brain might process information in an entirely different way 
than a digital computer does. Information might, for example, be pro- 
cessed globally the way a resistor analogue solves the problem of the 
minimal path through a network. Indeed, current evidence suggests that 
the neuron-switch model of the brain is no longer empirically tenable. 
Already in 1956 John von Neumann, one of the inventors of the modern 
digital computer, had his doubts: 

Now, speaking specifically of the human nervous system, this is an enormous 
mechanism at least 10 6 times larger than any artifact with which we are familiar 
and its activities are correspondingly varied and complex. Its duties include 
the interpretation of external sensory stimuli, of reports of physical and chemical 
conditions, the control of motor activities and of internal chemical levels, the 
memory function with its very complicated procedures for the transformation of 
and the search for information, and of course, the continuous relaying of coded 
orders and of more or less quantitative messages. It is possible to handle all these 
processes by digital methods (i.e., by using numbers and expressing them in the 
binary system or, with some additional coding tricks, in the decimal or some 
other system), and to process the digitalized, and usually numericized, informa- 
tion by algebraical (i.e., basically arithmetical) methods. This is probably the way 
a human designer would at present approach such a problem. The available 
evidence, though scanty and inadequate, rather tends to indicate that the human 
nervous system uses different principles and procedures. Thus message pulse trains 
seem to convey meaning by certain analogic traits (within the pulse notation 
i.e., this seems to be a mixed, part digital, part analog system), like the time 
density of pulses in one line, correlations of the pulse time series between different 
lines in a bundle, etc. 2 

Von Neumann goes on to spell out what he takes to be the "mixed 
character of living organisms." 

The neuron transmits an impulse. . . . The nerve impulse seems in the main to 
be an all-or-none affair, comparable to a binary digit. Thus a digital element is 
evidently present, but it is equally evident that this is not the entire story. 
... It is well known that there are various composite functional sequences in the 
organism which have to go through a variety of steps from the original stimulus 
to the ultimate effect some of the steps being neural, that is, digital, and others 
humoral, that is, analog. 3 



The Biological Assumption / 73 

But even this description grants too much to the digital model. It does 
not follow from the fact that the nerve impulse is an all-or-none affair 
that any digital processing at all is taking place. The distinction between 
digital and analogue computation is a logical distinction, not a distinc- 
tion based on the hardware or the sort of electrical impulses in the 
system. The essential difference between digital and analogue informa- 
tion processing is that in digital processing a single element represents 
a symbol in a descriptive language, that is, carries a specific bit of 
information; while in a device functioning as an analogue computer, 
continuous physical variables represent the information being processed. 
The brain, operating with volleys of pulses, would be a digital computer 
only if each pulse were correlated with some step in an information- 
processing sequence; if, however, the rate at which pulses are transmitted 
turns out to be the minimum unit which can be assigned a place in an 
information-processing model as von Neumann seems to hold then 
the brain would be operating as an analogue device. 4 * 

Once this conceptual confusion has been cleared up, von Neumann 
can be understood as suggesting that the brain functions exclusively like 
an analogue computer, and subsequent work has tended to confirm this 
hypothesis. Even for those unfamiliar with the technical details of the 
following report, the conclusion is clear: 

In the higher invertebrates we encounter for the first time phenomena such as 
the graded synaptic potential, which before any post-synaptic impulse has arisen 
can algebraically add the several incoming presynaptic barrages in a complex 
way. These incoming barrages are of a different value depending upon the 
pathway and a standing bias. Indeed, so much can be done by means of this 
graded and nonlinear local phenomenon prior to the initiation of any post- 
synaptic impulse that we can no more think of the typical synapse in integrative 
systems as being a digital device exclusively as was commonly assumed a few 
years ago, but rather as being a complex analog device. . . . 5 

The latest suggestion from Jerome Lettvin of M.I.T. is that the diame- 
ter of the axion may play a crucial role in processing information by 
acting as a filter. 6 An individual neuron fires at a certain frequency. The 
diameter of its various axion branches would act as low pass filters at 



What Computers Can't Do / 74 

different cutoff frequencies. Output from a given cell would then produce 
different frequencies at different terminals. The filter characteristics of 
the axion would vary with its diameter which in turn might be a function 
of the recency of signals passing down that axion, or even, perhaps, of 
the activation of immediately environing axions. If such time factors and 
field interactions play a crucial role, there is no reason to hope that the 
information processing on the neurophysiological level can be described 
in a digital formalism or, indeed, in any formalism at all. 

In 1966, Walter Rosenblith of M.I.T., one of the pioneers in the use 
of computers in neuropsychology, summed up the situation: 

We no longer hold the earlier widespread belief that the so-called all-or-none law 
from nerve impulses makes it legitimate to think of relays as adequate models 
for neurons. In addition, we have become increasingly impressed with the in- 
teractions that take place among neurons: in some instances a sequence of nerve 
impulses may reflect the activities of literally thousands of neurons in a finely 
graded manner. In a system whose numerous elements interact so strongly with 
each other, the functioning of the system is not necessarily best understood by 
proceeding on a neuron-by-neuron basis as if each had an independent personal- 
ity. . . . Detailed comparisons of the organization of computer systems and brains 
would prove equally frustrating and inconclusive. 7 

Thus the view that the brain as a general-purpose symbol-manipulat- 
ing device operates like a digital computer is an empirical hypothesis 
which has had its day. No arguments as to the possibility of artificial 
intelligence can be drawn from current empirical evidence concerning 
the brain. In fact, the difference between the "strongly interactive" 
nature of brain organization and the noninteractive character of machine 
organization suggests that insofar as arguments from biology are rele- 
vant, the evidence is against the possibility of using digital computers to 
produce intelligence. 



The Psychological Assumption 



Whether the brain operates like a digital computer is a strictly empiri- 
cal question to be settled by neurophysiology. The computer model 
simply fails to square with the facts. No such simple answer can be given 
to the related but quite different question: whether the mind functions 
like a digital computer, that is, whether one is justified in using a com- 
puter model in psychology. The issue here is much harder to define. The 
brain is clearly a physical object which uses physical processes to trans- 
form energy from the physical world. But if psychology is to differ from 
biology, the psychologist must be able to describe some level of function- 
ing other than the physical-chemical reactions in the brain. 

The theory we shall criticize claims that there is such a level the 
information-processing level and that on this level the mind uses com- 
puter processes such as comparing, classifying, searching lists, and so 
forth, to produce intelligent behavior. This mental level, unlike the physi- 
cal level, has to be introduced as a possible level of discourse. The issues 
involved in this discussion will, therefore, be philosophical rather than 
empirical. We shall see that the assumption of an information-processing 
level is by no means so self-evident as the cognitive simulators seem to 
think; that there are good reasons to doubt that there is any "information 
processing" going on, and therefore reason to doubt the validity of the 
claim that the mind functions like a digital computer. 



What Computers Can't Do / 76 



In 1957 Simon predicted that within ten years psychological theories 
would take the form of computer programs, and he set out to fulfill this 
prediction by writing a series of programs which were meant to simulate 
human cognition by simulating the conscious and unconscious steps a 
person goes through to arrive at a specific cognitive performance. And 
we have seen that despite the general inadequacy of such programs, 
admitted even by enthusiasts such as Minsky, all those involved in the 
general area of artificial intelligence (Minsky included) share the as- 
sumption that human beings, when behaving intelligently, are following 
heuristic rules similar to those which would be necessary to enable a 
digital computer to produce the same behavior. 

Moreover, despite meager results, Simon's prediction has nonetheless 
been partially fulfilled. There has been a general swing from behaviorism 
to mentalism in psychology. Many influential psychologists and philoso- 
phers of psychology have jumped on Simon's bandwagon and begun to 
pose their problems in terms of computer analogies. Ulric Neisser as- 
sumes that "the task of a psychologist trying to understand human 
cognition is analogous to that of a man trying to discover how a com- 
puter has been programmed." 1 And George Miller of Harvard now 
speaks of "recent developments in our understanding of man viewed as 
a system for processing information." 2 

Usually no argument is given for this new dogma that man is an 
information-processing system functioning like a heuristically pro- 
grammed digital computer. It seems rather to be an unquestioned axiom 
underlying otherwise careful and critical analysis. There is no doubt 
some temptation to suppose that since the brain is a physical thing and 
can be metaphorically described as "processing information," there must 
be an information-processing level, a sort of flow chart of its operations, 
in which its information-processing activity can be described. But we 
have seen in Chapter 3 that just because the brain is physical and pro- 
cesses information is no reason for biologists to suppose that it functions 
like a digital computer. The same holds for the psychological level. 
Although psychologists describe that function called the mind as "pro- 
cessing information," this does not mean that it actually processes infor- 



The Psychological Assumption / 77 

mation in the modern technical sense, nor that it functions like a digital 
computer, that is, that it has a program. 

"Information processing" is ambiguous. If this term simply means 
that the mind takes account of meaningful data and transforms them into 
other meaningful data, this is certainly incontrovertible. But the cyber- 
netic theory of information, introduced in 1948 by Claude Shannon, has 
nothing to do with meaning in this ordinary sense. It is a nonsemantic, 
mathematical theory of the capacity of communication channels to 
transmit data. A bit (binary digit) of information tells the receiver which 
of two equally probable alternatives has been chosen. 

In his classic paper "The Mathematical Theory of Communication" 
Shannon was perfectly clear that his theory, worked out for telephone 
engineering, carefully excludes as irrelevant the meaning of what is being 
transmitted. 

The fundamental problem of communication is that of reproducing at one point 
either exactly or approximately a message selected at another point. Frequently 
the messages have meaning; that is they refer to or are correlated according to 
some system with certain physical or conceptual entities. These semantic aspects 
of communication are irrelevant to the engineering problem. 3 

Warren Weaver in explaining the significance of Shannon's paper is 
even more emphatic: 

The word Information, in this theory, is used in a special sense that must not be 
confused with its ordinary usage. In particular, information must not be confused 
with meaning. 

In fact, two messages, one of which is heavily loaded with meaning and the 
other of which is pure nonsense, can be exactly equivalent, from the present 
viewpoint, as regards information. It is this, undoubtedly, that Shannon means 
when he says that "the semantic aspects of communication are irrelevant to the 
engineering aspects." 4 

When illegitimately transformed into a theory of meaning, in spite of 
Shannon's warning, information theory and its vocabulary have already 
built in the computer-influenced assumption that experience can be 
analyzed into isolable, atomic, alternative choices. As a theory of mean- 
ing this assumption is by no means obvious. Gestalt psychologists, for 
example, claim (as we have seen in Part I and shall argue in detail in Part 



What Computers Can't Do / 78 

III) that thinking and perception involve global processes which cannot 
be understood in terms of a sequence or even a parallel set of discrete 
operations. 5 * Just as the brain seems to be, at least in part, an analogue 
computer, so the mind may well arrive at its thoughts and perceptions 
by responding to "fields," "force," "configurations," and so on, as, in 
fact, we seem to do insofar as our thinking is open to phenomenological 
description. 6 

It is precisely the role of the programmer to make the transition from 
statements which are meaningful (contain information in the ordinary 
sense) to the strings of meaningless discrete bits (information in the 
technical sense) with which a computer operates. The ambition of artifi- 
cial intelligence is to program the computer to do this translating job 
itself. But it is by no means obvious that the human translator can be 
dispensed with. 

Much of the literature of Cognitive Simulation gains its plausibility by 
shifting between the ordinary use of the term "information" and the 
special technical sense the term has recently acquired. Philosophical 
clarity demands that we do not foreclose the basic question whether 
human intelligence presupposes rulelike operations on discrete elements 
before we begin our analysis. Thus we must be careful to speak and think 
of "information processing" in quotation marks when referring to hu- 
man beings. 

Moreover, even if the mind did process information in Shannon's sense 
of the term, and thus function like a digital computer, there is no reason 
to suppose that it need do so according to a program. If the brain were 
a network of randomly connected neurons, there might be no flow chart, 
no series of rule-governed steps on the information-processing level, 
which would describe its activity. 

Both these confusions the step from ordinary meaning to the techni- 
cal sense of information and from computer to heuristically programmed 
digital computer are involved in the fallacy of moving from the fact 
that the brain in some sense transforms its inputs to the conclusion that 
the brain or mind performs some sequence of discrete operations. This 
fallacy is exhibited in the baldest form in a recent paper by Jerry Fodor. 
It is instructive to follow his argument. 



The Psychological Assumption / 79 

Fodor begins with generally accepted facts about the central nervous 
system: 

If the story about the causal determination of depth estimates by texture gradi- 
ents is true and if the central nervous system is the kind of organ most sensitive 
people now think it is, then some of the things the central nervous system does, 
some of the physical transactions that take place in the central nervous system 
when we make estimates of depth, must satisfy such descriptions as 'monitoring 
texture gradients', 'processing information about texture gradients', 'computing 
derivatives of texture gradients', etc. 7 

He thus arrives at the view that "every operation of the nervous system 
is identical with some sequence of elementary operations." 8 

Disregarding the question-begging use of "processing information" in 
this account, we can still object that computing the first derivative of a 
texture gradient is the sort of operation very likely to be performed by 
some sort of analogue device. There is, therefore, no reason at all to 
conclude from the fact that the nervous system responds to differences 
in texture gradients that "every operation of the nervous system is identi- 
cal with some sequence of elementary operations. . . ." There is, indeed, 
not the slightest justification for the claim that "for each type of behavior 
in the repertoire of that organism, a putative answer to the question, 
How does one produce behavior of that type? takes the form of a set 
of specific instructions for producing the behavior by performing a set 
of machine operations." 9 

The argument gains its plausibility from the fact that if a psychologist 
were to take the first derivative of a texture gradient, he would compute 
it using a formalism (differential calculus) which can be manipulated in 
a series of discrete operations on a digital computer. But to say that the 
brain is necessarily going through a series of operations when it takes the 
texture gradient is as absurd as to say that the planets are necessarily 
solving differential equations when they stay in their orbits around the 
sun, or that a slide rule (an analogue computer) goes through the same 
steps when computing a square root as does a digital computer when 
using the binary system to compute the same number. 

Consider an ion solution which might be capable of taking a texture 
gradient or of simulating some other perceptual process by reaching 



What Computers Can't Do / 80 

equilibrium. Does the solution, in reaching equilibrium, go through the 
series of discrete steps a digital computer would follow in solving the 
equations which describe this process? In that case, the solution is solv- 
ing in moments a problem which it would take a machine centuries to 
solve if the machine could solve it at all. Is the solution an ultrarapid 
computer, or has it got some supposedly clever heuristic like the chess 
master, which simplifies the problem? Obviously, neither. The fact that 
we can describe the process of reaching equilibrium in terms of equations 
and then break up these equations into discrete elements in order to solve 
them on a computer does not show that equilibrium is actually reached 
in discrete steps. Likewise, we need not conclude from the fact that all 
continuous physicochemical processes involved in human "information 
processing" can in principle be formalized and calculated out discretely, 
that any discrete processes are actually taking place. 

Moreover, even if one could write such a computer program for simu- 
lating the physicochemical processes in the brain, it would be no help to 
psychology. 

If simulation is taken in its weakest possible sense, a device is simu- 
lated by any program which realizes the same input/output function 
(within the range of interest). Whether achievable for the brain or not, 
this clearly lacks what is necessary for a psychological theory, namely 
an account of how the mind actually "works." For psychological expla- 
nation, a representation, somehow stronger than a mere simulation, is 
required. As Fodor notes: 

We can say that a machine is strongly equivalent to an organism in some respect 
when it is weakly equivalent in that same respect and the processes upon which 
the behavior of the machine is contingent are of the same type as the processes 
upon which the behavior of the organism are contingent. 10 

That is, equivalence in the psychological respect demands machine pro- 
cesses, of the psychological type. n * Psychological operations must be the 
sort which human beings at least sometimes consciously perform when 
processing information for example, searching, sorting, and storing 
and not physicochemical processes in the organism. Thus a chess player's 



The Psychological Assumption / 57 

report as he zeroed in on his Rook, "And now my brain reaches the 
following chemical equilibrium, described by the following array of diff- 
erential equations," would describe physiological processes no doubt 
correlated with "information processing," but not that "information 
processing" itself. 

Fodor is not clear whether his argument is supposed to be a priori or 
empirical, that is, whether or not he thinks it follows logically or merely 
contingently from the claim that the brain is taking account of the 
texture gradient that it is performing a sequence of elementary opera- 
tions. The fact that he chooses this example, which is one of the least 
plausible cases in which one would want to argue that the brain or the 
mind is performing any elementary operations at all, suggests that he 
thinks there is some kind of necessary connection between taking a 
texture gradient, computing, and performing a sequence of operations. 
When this argument is shown to be a series of confusions, however, the 
advocates of the psychological assumption can always shift ground and 
claim that theirs is not an a priori argument but an empirical conclusion 
based on their experiments. 

Fodor took this tack in defending his paper at the meeting of the 
American Philosophical Association at which it was delivered, while 
Miller et al. justify their work strictly on the basis of what they take to 
be the success of CS. 

A Plan is, for an organism, essentially the same as a program for a computer. 
. . . Newell, Shaw, and Simon have explicitly and systematically used the hier- 
archical structure of lists in their development of "information-processing lan- 
guages" that are used to program high-speed digital computers to simulate 
human thought processes. 

Their success in this direction which the present authors find most impressive 
and encouraging argues strongly for the hypothesis that a hierarchical structure 
Is the basic form of organization of human problem-solving. 12 

We have seen in Part I that Newell, Shaw, and Simon's results are 
far from impressive. What then is this encouraging empirical evidence? 
We must now look at the way Newell, Shaw, and Simon's work is 
evaluated. 



What Computers Can't Do / 82 



I. Empirical Evidence for the Psychological 
Assumption: Critique of the Scientific 
Methodology of Cognitive Simulation 

The empirical justification of the psychological assumption poses a ques- 
tion of scientific methodology the problem of the evaluation of evi- 
dence. Gross similarities of behavior between computers and people do 
not justify the psychological asumption, nor does the present inability to 
demonstrate these similarities in detail alone justify its rejection. A test 
of the psychological assumption requires a detailed comparison of the 
steps involved in human and machine information processing. As we 
have seen (Chapter 1, Sec. II), Newell, Shaw, and Simon conscientiously 
note the similarities and differences between human protocols and ma- 
chine traces recorded during the solution of the same problem. We must 
now turn to their evaluation of the evidence thus obtained. 
Newell and Simon conclude that their work 

provide[s] a general framework for understanding problem-solving behavior 
. . . and finally reveals with great clarity that free behavior of a reasonably 
intelligent human can be understood as the product of a complex but finite and 
determinate set of laws. 13 

This is a strangely unscientific conclusion to draw, for Newell and Simon 
acknowledge that their specific theories like any scientific theories 
must stand or fall on the basis of their generality, that is, the range of 
phenomena which can be explained by the programs. 14 Yet their program 
is nongeneral in at least three ways. The available evidence has neces- 
sarily been restricted to those most favorable cases where the subject can 
to some extent articulate his information-processing protocols (game 
playing and the solution of simple problems) to the exclusion of pattern 
recognition and the acquisition and use of natural language. Moreover, 
even in these restricted areas the machine trace can only match the 
performance of one individual, and only after ad hoc adjustments. And 
finally, even the match is only partial. Newell and Simon note that their 
program "provides a complete explanation of the subject's task behavior 



The Psychological Assumption / 83 

with five exceptions of varying degrees of seriousness." 15 

In the light of these restrictions it is puzzling how Newell and Simon 
can claim a "general framework," and in the light of the exceptions it 
is hard to see how they can claim to have any kind of scientific under- 
standing at all. There seems to be some confusion here concerning the 
universality of scientific laws or theories. In general, scientific laws do 
not admit of exceptions, yet here the exceptions are honestly noted as 
if the frank recognition of these exceptions mitigates their importance; 
as if Galileo might, for example, have presented the law of falling bodies 
as holding for all but five objects which were found to fall at a different 
rate. Not that a scientific conjecture must necessarily be discarded in the 
face of a few exceptions; there are scientifically sanctioned ways of deal- 
ing with such difficulties. One can, to begin with, hold on to the generali- 
zation as a working hypothesis and wait to announce a scientific law until 
the exceptions are cleared up. A working hypothesis need not explain all 
the data. When, however, the scientist claims to present a theory, let 
alone a "general framework for understanding," he must deal with these 
exceptions either by subsuming them under the theory (as in the appeal 
to friction to explain deviations from the laws of motion), or by suggest- 
ing where to look for an explanation, or at least by showing how, accord- 
ing to the theory, one would expect such difficulties. Newell and Simon 
take none of these lines. 

They might argue that there is no cause for concern, that there are 
exceptions to even the best theories. In his study of scientific revolutions, 
Thomas Kuhn notes the persistence of anomalies in all normal science. 

There are always some discrepancies. . . . Even the most stubborn ones usually 
respond at last to normal practice. Very often scientists are willing to wait, 
particularly if there are many problems available in other parts of the field. We 
have already noted, for example, that for sixty years after Newton's original 
computation, the predicted motion of the moon's perigee remained only half of 
that observed. 16 

But this cannot be a source of comfort for Newell and Simon. Such 
tolerance of anomalies assumes that there already is an ongoing science, 
an "accepted paradigm" which "must seem better than its competi- 



What Computers Can't Do / 84 

tors." 17 This supposes that the theory works perfectly in at least some 
clearly defined area. But Newell and Simon's cognitive theory is not only 
not general. It does not work even in a carefully selected special case. It 
is just where we would have to find a perfect match in order to establish 
a paradigm that we find the exceptions. Thus Newell and Simon's work, 
even though it offers some surprising approximations, does not establish 
a functioning science which would justify a claim to have found general 
laws even in the face of anomalies. 

In discussing the Newtonian anomaly above, Kuhn goes on to point 
out that "Europe's best mathematical physicists continued to wrestle 
unsuccessfully with the well-known discrepancy. . . ." l8 The absence of 
this sort of concern further distinguishes Newell and Simon's work from 
normal scientific practice. After noting their exceptions, no one in CS 
least of all Newell and Simon seems interested in trying to account for 
them. Rather all go on to formulate, in some new area, further ad hoc 
rough generalizations. 

There is one other acceptable way of dealing with exceptions. If one 
knew, on independent grounds, that mental processes must be the prod- 
uct of a rule-governed sequence of discrete operations, then exceptions 
could be dealt with as accidental difficulties in the experimental tech- 
nique, or challenging cases still to be subsumed under the law. Only then 
would those involved in the field have a right to call each program which 
simulated intelligent behavior no matter how approximately an 
achievement and to consider all setbacks nothing but challenges for 
sharper heuristic hunting and further programming ingenuity. The prob- 
lem, then, is how to justify independently the assumption that all human 
"information processing" proceeds by discrete steps. Otherwise the ex- 
ceptions, along with the narrow range of application of the programs and 
the lack of progress during the last ten years, would tend to disconfirm 
rather than confirm this hypothesis. The "justification" seems to have 
two stages. 

In the early literature, instead of attempting to justify this important 
and questionable digital-assumption, Newell and Simon present it as a 
postulate, a working hypothesis which directs their investigation. "We 



The Psychological Assumption / 85 

postulate that the subject's behavior is governed by a program organized 
from a set of elementary information processes." 19 This postulate, which 
alone might seem rather arbitrary, is in turn sanctioned by the basic 
methodological principle of parsimony. According to Newell, Shaw, and 
Simon, this principle enjoins us to assume tentatively the most simple 
hypothesis, in this case that all information processing resembles that 
sort of processing which can be programmed on a digital computer. We 
can suppose, for example, that in chess, when our subject is zeroing in, 
he is unconsciously counting out. In general, whenever the machine trace 
shows steps which the subject did not report, the principle of parsimony 
justifies picking a simple working hypothesis as a guide to experimenta- 
tion and assuming that the subject unconsciously went through these 
steps. But of course further investigation must support the working 
hypothesis; otherwise, it must eventually be discarded. 

The divergence of the protocols from the machine trace, as well as the 
difficulties raised by planning, indicate that things are not so simple as 
our craving for parsimony leads us to hope. In the light of these difficul- 
ties, it would be natural to revise the working hypothesis, just as scien- 
tists had to give up Newtonian Mechanics when it failed to account for 
certain observations; but at this point, research in Cognitive Simulation 
deviates from acceptable scientific procedures. In summarizing their 
work in CS, Newell and Simon conclude: 

There is a growing body of evidence that the elementary information processes 
used by the human brain in thinking are highly similar to a subset of the 
elementary information processes that are incorporated in the instruction codes 
of the present-day computers. 20 

What is this growing body of evidence? Have the gaps in the proto- 
cols been filled and the exceptions explained? Not at all. The growing 
body of evidence seems to be the very programs whose lack of univer- 
sality would cast doubt on the whole project but for the independent 
assumption of the digital hypothesis. In the face of the exceptions, the 
psychological assumption would have to already have been taken 
as independently justified, for the specific programs to be presented as 
established theories; yet now the assumption is recognized as an hypothe- 



What Computers Can't Do / 86 

sis whose sole confirmation rests on the success of the specific programs. 
An hypothesis based on a methodological principle is often confirmed 
later by the facts. What is unusual and inadmissible is that, in this case, 
the hypothesis produces the evidence by which it is later confirmed. 

No independent, empirical evidence exists for the psychological as- 
sumption. In fact, the same empirical evidence presented for the assump- 
tion that the mind functions like a digital computer tends, when 
considered without making this assumption, to show that the assumption 
is empirically untenable. 

This particular form of methodological confusion is restricted to those 
working in Cognitive Simulation, but even workers in Artificial Intelli- 
gence share this belief in the soundness of heuristic programs, this ten- 
dency to think of all difficulties as accidental, and this refusal to consider 
any setbacks as discontinuing evidence. Concluding from the small area 
in which search procedures are partially successful, workers of both 
schools find it perfectly clear that the unknown and troublesome areas 
are of exactly the same sort. Thus, all workers proceed as if the credit 
of the psychological assumption were assured, even if all do not like 
those in Cognitive Simulation attempt to underwrite the credit with a 
loan for which it served as collateral. For workers in the field, the 
psychological assumption seems not to be an empirical hypothesis that 
can be supported or disconfirmed, but some sort of philosophical axiom 
whose truth is assured a priori. 

II. A Priori Arguments for the Psychological 
Assumption 

A clue to the a priori character of this axiom can be gained from another 
look at the way Miller et al. introduce their computer model. The same 
page which concludes that Simon's success argues strongly for their 
position opens with a statement of their aims: 

Any complete description of behavior should be adequate to serve as a set of 
instructions, that is, it should have the characteristics of a plan that could guide 
the action described. 21 



The Psychological Assumption / 87 

Miller et al. assume that our very notion of explanation or complete 
description requires that behavior be described in terms of a set of 
instructions, that is, a sequence of determinate responses to determinate 
situations. No wonder psychologists such as Newell, Neisser, and Miller 
find work in Cognitive Simulation encouraging. In their view, if psychol- 
ogy is to be possible at all, an explanation must be expressible as a 
computer program. This is not an empirical observation but follows from 
their definition of explanation. Divergences from the protocol and fail- 
ures can be ignored. No matter how ambiguous the empirical results in 
Cognitive Simulation, they must be a first step toward a more adequate 
theory. 

This definition of explanation clearly needs further investigation: Does 
it make sense? Even if it does, can one prejudge the results in psychology 
by insisting theories must be computer programs because otherwise psy- 
chology isn't possible? Perhaps, psychology as understood by the cogni- 
tive simulationists is a dead end. 

To begin with it is by no means clear what the pronouncement that 
a complete description must take the form of a set of instructions means. 
Consider the behavior involved in selecting, on command, a red square 
from a multicolored array of geometrical figures. A complete description 
of that behavior according to Miller et al. would be a set of instructions, 
a plan to follow in carrying out this task. What instructions could one 
give a person about to undertake this action? Perhaps some very general 
rules such as listen to the instructions, look toward the objects, consider 
the shapes, make your selection. But what about the detailed instructions 
for identifying a square rather than a circle? One might say: "Count the 
sides; if there are four, it is a square." And what about the instructions 
for identifying a side? "Take random points and see if they fall on a line 
which is the shortest distance between the end points," and so on. And 
how does one find these points? After all, there are no points in the field 
of experience when I am confronting a display of geometrical figures. 
Perhaps here the instructions run out and one just says: "But you uncon- 
sciously see points and unconsciously count." But do you? And why do 
the instructions stop here and not earlier or later? And if all this does 



What Computers Can't Do / 88 

not seem strange enough, what instructions do you give someone for 
distinguishing red from blue? At this point it is no longer clear why or 
how a complete description in psychology should take the form of a set 
of instructions. 

Still such a claim is the heir to a venerable tradition. Kant explicitly 
analyzed all experience, even perception, in terms of rules, and the no- 
tion that knowledge involves a set of explicit instructions is even older. 
In fact, we have seen that the conviction that a complete description 
involving an analysis into instructions must be possible, because only 
such an analysis enables us to understand what is going on, goes back 
to the beginning of philosophy, that is, to the time when our concepts 
of understanding and reason were first formulated. Plato, who formu- 
lated this analysis of understanding in the Euthyphro, goes on to ask in 
the Meno whether the rules required to make behavior intelligible to the 
philosopher are necessarily followed by the person who exhibits the 
behavior. That is, are the rules only necessary if the philosopher is to 
understand what is going on, or are these rules necessarily followed by 
the person insofar as he is able to behave intelligently? Since Plato 
generally thought of most skills as just pragmatic puttering, he no doubt 
held that rules were not involved in understanding (or producing) skilled 
behavior. But in the case of theorem proving or of moral action, Plato 
thought that although people acted without necessarily being aware of 
any rules, their action did have a rational structure which could be 
explicated by the philosopher, and he asks whether the mathematician 
and the moral agent are implicitly following this program when behaving 
intelligently. 

This is a decisive issue for the history of our concepts of understanding 
and explanation. Plato leaves no doubt about his view: any action which 
is in fact sensible, i.e., nonarbitrary, has a rational structure which can 
be expressed in terms of some theory and any person taking such action 
will be following, at least implicitly, this very theory taken as a set of 
rules. For Plato, these instructions are already in the mind, prepro- 
grammed in a previous life, and can be made explicit by asking the 
subjects the appropriate questions. 22 Thus, for Plato, a theory of human 
behavior which allows us to understand what a certain segment of that 



The Psychological Assumption / 89 

behavior accomplishes is also an explanation of how that behavior is 
produced. Given this notion of understanding and this identification of 
understanding and explanation, one is bound to arrive at the cognitive 
simulationists with their assumption that it is self-evident that a complete 
description of behavior is a precise set of instructions for a digital com- 
puter, and that these rules can actually be used to program computers 
to produce the behavior in question. 

We have already traced the history of this assumption that thinking 
is calculating. 23 We have seen that its attraction harks back to the Pla- 
tonic realization that moral life would be more bearable and knowledge 
more definitive if it were true. Its plausibility, however, rests only on a 
confusion between the mechanistic assumptions underlying the success 
of modern physical science and a correlative formalistic assumption 
underlying what would be a science of human behavior if such existed. 

On one level, this a priori assumption makes sense. Man is an object. 
The success of modern physical science has assured us that a complete 
description of the behavior of a physical object can be expressed in precise 
laws, which in turn can serve as instructions to a computer which can 
then, at least in principle, simulate this behavior. This leads to the idea 
of a neurophysiological description of human behavior in terms of inputs 
of energy, physical-chemical transactions in the brain, and outputs in 
terms of motions of the physical body, all, in principle, simulatable on 
a digital machine. 

This level of description makes sense, at least at first approximation, 
and since the time of Descartes has been part of the idea of a total 
physical description of all the objects in the universe. The brain is clearly 
an energy-transforming organ. It detects incoming signals; for example, 
it detects changes in light intensity correlated with changes in texture 
gradient. Unfortunately for psychologists, however, this physical de- 
scription, excluding as it does all psychological terms, is in no way a 
psychological explanation. On this level one would not be justified in 
speaking of human agents, the mind, intentions, perceptions, memories, 
or even of colors or sounds, as psychologists want to do. Energy is being 
received and transformed and that is the whole story. 

There is, of course, another level let us call it phenomenological 



What Computers Can't Do / 90 

on which it does make sense to talk of human agents, acting, perceiving 
objects, and so forth. On this level what one sees are tables, chairs, and 
other people, what one hears are sounds and sometimes words and 
sentences, and what one performs are meaningful actions in a context 
already charged with meaning. But this level of description is no more 
satisfactory to a psychologist than the physiological level, since here 
there is no awareness of following instructions or rules; there is no place 
for a psychological explanation of the sort the cognitive simulationist 
demands. Faced with this conceptual squeeze, psychologists have always 
tried to find a third level on which they can do their work, a level which 
is psychological and yet offers an explanation of behavior. 

If psychology is to be a science of human behavior, it must study man 
as an object. But not as a physical object, moving in response to inputs 
of physical energy, since that is the task of physics and neurophysiology. 
The alternative is to try to study human behavior as the response of some 
other sort of object to some other sort of input. Just what this other sort 
of object and input are is never made clear, but whatever they are, if there 
is to be an explanation, man must be treated as some device responding 
to discrete elements, according to laws. These laws can be modeled on 
causal laws describing how fixed propensities in the organism interact 
with inputs from the environment to produce complex forms of behavior. 
The device, then, is a reflex machine, and the laws are the laws of 
association. This gives us the empiricist psychology of David Hume with 
its modern descendant, S-R psychology. Or the object can be treated as 
an information-processing device and the laws can be understood on the 
Kantian model, as reasons, which are rules in the mind applied by the 
mind to the input. In psychology this school was called idealist, intellec- 
tualist, or mentalist, and is now called "cognitive psychology." 

Until the advent of the computer the empiricist school had the edge 
because the intellectualist view never succeeded in treating man as a 
calculable object. There was always a subject, a "transcendental ego," 
applying the rules, which simply postponed a scientific theory of behav- 
ior by installing a little man (homunculus) in the mind to guide its 
actions. Computers, however, offer the irresistible attraction of operating 
according to rules without appeal to a transcendental ego or homun- 



The Psychological Assumption / 91 

culus. Moreover, computer programs provide a model for the analysis 
of behavior such as speaking a natural language which seems to be too 
complex to be accounted for in terms of S-R psychology. In short, there 
is now a device which can serve as a model for the mentalist view, and 
it is inevitable that regardless of the validity of the arguments or the 
persuasiveness of the empirical evidence, psychologists dissatisfied with 
behaviorism will clutch at this high-powered straw. 

A computer is a physical object, but to describe its operation, one does 
not describe the vibrations of the electrons in its transistors, but rather 
the levels of organization of its on/off flip/flops. If psychological con- 
cepts can be given an interpretation in terms of the higher levels of 
organization of these rule-governed flip/flops, then psychology will have 
found a language in which to explain human behavior. 

The rewards are so tempting that the basic question, whether this third 
level between physics and phenomenology is a coherent level of discourse 
or not, is not even posed. But there are signs of trouble. The language 
of books such as those by Miller et al., Neisser, and Fodor is literally 
incoherent. On almost every page one finds sentences such as the fol- 
lowing: 

When an organism executes a Plan he proceeds step by step, completing one part 
and then moving to the next. 24 * 

Here all three levels exist in unstable and ungrammatical suspension. 
"When an organism [biological] executes [machine analogy borrowed 
from human agent] a Plan he [the human agent] . . ." Or, one can have 
it the other way around and instead of the organism being personified, 
one can find the mind mechanized. Fodor speaks of "mental process- 
ing," 25 or "mental operations," 26 as if it were clear what such a form of 
words could possibly mean. 

This new form of gibberish would merely be bizarre if it did not reveal 
more serious underlying conceptual confusions. These are implicit in the 
work of Miller et al. but become clear in the works of Neisser and Fodor, 
who, of all the writers in this area, make the greatest effort to articulate 
their philosophical presuppositions. The confusion can best be brought 
to light by bearing firmly in mind the neurophysiological and phe- 



What Computers Can't Do / 92 

nomenological levels of description and then trying to locate the psycho- 
logical level somewhere between these two. 

In trying to make a place for the information-processing level Neisser 
tells us: 

There is certainly a real world of trees and people and cars and even books. 
. . . However, we have no immediate access to the world nor to any of its 
properties. 27 

This is certainly true insofar as man is regarded as a physical object. 28 * 
As Neisser puts it, ". . . the sensory input is not the page itself; it is 
pattern of light rays. . . ." 29 So far so good, but then, Neisser goes on to 
bring the physical and the phenomenological levels together: "Suitably 
focussed by the lens . . . the rays fall on the sensitive retina, where they 
can initiate the neural processes that eventually lead to seeing and reading 
and remembering. " 30 Here, however, things are by no means obvious. 
There are two senses of "lead to." Light waves falling on the retina 
eventually lead to physical and chemical processes in the brain, but in 
this sequential sense, the light rays and neural processes can never even- 
tually lead to seeing. 31 * Seeing is not a chemical process; thus it is not 
a final step in a series of such processes. If, on the other hand, "lead to" 
means "necessary and sufficient condition for," then, either seeing is the 
whole chain or something totally different from the chain or any link of 
it. In either case it is no longer clear why Neisser says we have no 
immediate access to the perceptual world. 

Once the neural and phenomenological levels have thus been illegiti- 
mately amalgamated into one series, which stands between the person 
and the world, a new vocabulary is required. This no-man's-land is 
described in terms of "sensory input" and its "transformations." 

As used here, the term "cognition" refers to all the processes by which the 
sensory input is transformed, reduced, elaborated, stored, recovered, and used. 
. . . Such terms as sensation, perception, imagery, retention, recall, problem- 
solving, and thinking, among many others, refer to hypothetical stages or aspects 
of cognition. 32 

Once a "sensory input" which differs from the world we normally see 
has been introduced, it seems necessary that our perception be "devel- 



The Psychological Assumption / 93 

oped from," or a "transformation of this "stimulus input." 33 * But what 
this transformation means depends on the totally ambiguous notion of 
"stimulus input." If the input is energy, then it is only necessary that it 
be transformed into other energy the processes in the brain are surely 
physical from beginning to end. Matter-energy can be transformed, re- 
duced, elaborated, stored, recovered, and used, but it will never be any- 
thing but matter-energy. If, however, the stimulus is some sort of 
primitive perception, as Neisser later seems to suggest "a second stimu- 
lus will have some effect on how the first brief one is perceived" 34 then 
we have to know more about what this new percept is. Philosophers have 
ceased to believe in sense data, and if Neisser has some notion of a 
primitive percept, it cannot be introduced without a great deal of argu- 
ment and evidence. Phenomenologically we directly perceive physical 
objects. We are aware of neither sense data nor light rays. If Neisser 
wants to shift his notion of input from physical to perceptual, it is up to 
him to explain what sort of perception he has in mind, and what evidence 
he has that such a percept, which is neither a pattern of light rays nor 
a perspectival view of a physical object, exists. 35 * 

"Information" is the concept which is supposed to rescue us from this 
confusion. Neisser says "Information is what is transformed, and the 
structured pattern of its transformation is what we want to under- 
stand." 36 But as long as the notion of "stimulus input" is ambiguous, it 
remains unclear what information is and how it is supposed to be related 
to the "stimulus input," be it energy or direct perception. 

Finally, in a dazzling display of conceptual confusion, these two inter- 
dependent and ambiguous notions, "stimulus input" and "information," 
are combined in the "central assertion" of the book: 

The central assertion is that seeing, hearing, and remembering are all acts of 
construction, which may make more or less use of stimulus information [sic] 
depending on circumstances. The constructive processes are assumed to have two 
stages of which the first is fast, crude, wholistic, and parallel, while the second 
is deliberate, attentive, detailed, and sequential. 37 

The ambiguity of "stimulus information" and the subsequent incoher- 
ence of the conceptual framework underlying this approach and its 



What Computers Can't Do / 94 

consequences can best be seen by following a specific example. Let us 
take Neisser's analysis of the perception of a page. 

If we see moving objects as unified things, it must be because perception results 
from an integrative process over time. The same process is surely responsible for 
the construction of visual objects from the successive "snapshots" taken by the 
moving eye. 38 

The question to be asked here is: What are these snapshots? Are they 
"patterns of energy" or are they momentary pictures of a page? If they 
are patterns of energy they are in no sense perceived, and are integrated 
not by the subject (the perceiver) but by the brain as a physical object. 
On the other hand, on the phenomenological level, we do not have to 
integrate distinct snapshots of the page at all. The page is steadily seen, 
and the notion that it is seen as a series of "snapshots" or "inputs" is 
an abstraction from this continuously presented page. Of course, this 
steadily seen page is correlated with some "processing," but not the 
processing of rudimentary perceptual objects, or "snapshots" which 
could only give rise to the question of how these elementary perceptual 
objects were themselves "constructed" but the processing of some fluc- 
tuating pattern of energy bombarding the eye. 

This conceptual confusion, which results from trying to define a level 
of discourse between the physiological and the phenomenological, is even 
more pronounced in Fodor's work, because he tries even harder to be 
clear on just these points. In discussing the perception of visual and 
acoustic patterns Fodor notes that "the concept you have of a face, or 
a tune, or a shape . . . includes a representation of the formal structure 
of each of these domains and the act of recognition involves the applica- 
tion of such information to the integration of current sensory inputs." 39 

One wonders again what "sensory input" means here. If the "sensory 
input" is already a face, or a tune, or a shape, then the job is already done. 
On the other hand, if the "sensory input" is the physical energy reaching 
the sense organ, then it is impossible to understand what Fodor means 
by the "application" of a "concept" or of "information" to the integra- 
tion of such inputs, since what would integrate such physical energy 
would surely be further energy transformations. 



The Psychological Assumption / 95 

Of course, if we begged the question and assumed that the brain is a 
digital computer, then sense could be made of the notion that a concept 
is a formal structure for organizing data. In that case the "sensory input" 
would be neither a percept nor a pattern of energy, but a series of bits, 
and the concept would be a set of instructions for relating these bits to 
other bits already received, and classifying the result. This would amount 
to an hypothesis that human behavior can be understood on the model 
of a digital computer. It would require a theory of just what these bits 
are and would then have to be evaluated on the basis of empirical 
evidence. 

But for Fodor, as for Miller et al., the notion of "sensory input" and 
of a concept as a rule for organizing this input seems to need no justifica- 
tion but rather to be contained in the very notion of a psychological 
explanation. 

Insofar as it seeks to account for behavior, a psychological theory may be thought 
of as a function that maps an infinite set of possible inputs to an organism onto 
an infinite set of possible outputs. 4 



., 40 



As a conceptual analysis of the relation of perception and behavior, 
which is supposed to be accepted independently of empirical assump- 
tions about the brain, such an account is incomprehensible. 

As with Neisser, this incoherence can best be seen in a specific case. 
Fodor takes up the problem of how "we have learned to hear as similar" 
as one melody "what may be physically quite different sequences of 
tones." 41 Here the question-begging nature of the analysis is clear: Are 
these sequences of tones physical or phenomenal? Are they patterns of 
sound waves or percepts? The talk of their physical difference suggests 
the former. And indeed on the level of physical energy it is no doubt true 
that inputs of energy of various frequencies are correlated with the same 
perceptual experience. The energy transformations involved will pre- 
sumably someday be discovered by neurophysiologists. But such physical 
sequences of tones cannot be "heard" we do not hear frequencies; we 
hear sounds : and thus a fortiori these frequencies cannot be "heard as 
similar." If, on the other hand, we try to understand the input as se- 
quences of phenomenal tones, which it would make sense to "hear as 



What Computers Can't Do / 96 

similar," then we are on the level of perception, and unfortunately for 
Fodor the problem of how we hear these sequences of tones as similar 
vanishes; for in order to pose the problem in the first place we have 
already assumed that the phenomenal tone sequences are heard as simi- 
lar. On the phenomenal level we hear them as similar because they sound 
similar. 

To put it another way, Fodor speaks of "which note in particular (i.e., 
which absolute values of key, duration, intensity, stress, pitch, ampli- 
tude, etc.) we expect after hearing the first few notes of a performance 
of Lilliburlero. . . ." 42 But we do not "expect" any "absolute values" at 
all. We expect notes in a melody. The absolute values pose a problem for 
the neurophysiologist with his oscilloscope, or for someone hearing the 
notes in isolation, not for the perceiver. 

If we did perceive and expect these "absolute values," we would indeed 
need the "elaborate conceptualism" defended by Fodor, in order to 
recognize the same melody in various sequences: 

It is unclear how to account for the ability to recognize identity of type despite 
gross variations among tokens unless we assume that the concepts employed in 
recognition are of formidable abstractness. But then it is unclear how the applica- 
tion of such concepts ... is to be explained, unless one assumes psychological 
mechanisms whose operations must be complicated in the extreme. 43 

Here the confusion shows up in the use of "token" and "type." What 
are these tokens? The perceived phenomenal sound sequence (the mel- 
ody) cannot be an abstraction (a type) of which the physical energy in- 
puts are instantiations (tokens). The percept and the physical energy 
are equally concrete and are totally different sorts of phenomena. No 
amount of complication can bridge the gap between shifting energy 
inputs and the perception of an enduring sound. One is not an instantia- 
tion of the other. But neither can the tokens be taken to be the phenome- 
nal sequence of isolated absolute tones (as a sense data theorist would 
have it). In listening to a melody absolute tones are not perceived, so 
under this interpretation there would be no tokens at all. 

Even if one assumes that Fodor has in mind the physical model, which 
could be computerized, this kind of pattern recognition could conceiva- 



The Psychological Assumption / 97 

bly be accomplished by a neural net or by an analogue device, if it could 
be accomplished at all. There is no reason to suppose that it is accom- 
plished by a heuristic program (a set of abstract concepts), let alone that 
such a program is a conceptual necessity. 

Yet Fodor never questions the assumption that there is an informa- 
tion-processing level on which energy transformation can be discussed 
in terms of a sequence of specific operations. His only question is: How 
can we tell that we and the machine have the same program, that is, 
perform the same operations? Thus, for example, after asking how one 
could know whether one had a successful machine simulation, Fodor 
replies: ". . . we need only accept the convention that we individuate 
forms of behavior by reference not solely to the observable gestures 
output by an organism but also to the sequence of mental operations that 
underlie those gestures. "** 

Or even more baldly: 

strong equivalence requires that the operations that underlie the production of 
machine behavior be of the same type as the operations that underlie the produc- 
tion of organic behavior. 45 

It should now be clear that Fodor's argument depends on two sorts 
of assumptions: First, like Miller et al. and Neisser, he introduces the 
ambiguous notion of "input" to allow a level of description on which it 
seems to make sense to analyze perception as if man were a computer 
receiving some sort of data called "stimulus information." This amounts 
to the assumption that besides energy processing, "data processing is 
involved in perception." 46 

Fodor then makes two further assumptions of a second sort, of which 
he seems to be unaware: (1) that this data processing takes place as if on 
a digital computer, that is, consists of discrete operations, and (2) that 
this digital computer operates serially according to something like a 
heuristic program, so that one can speak of a sequence of such opera- 
tions. Fodor's defense of his "elaborate conceptualism," of his notion 
that perception requires complicated mental operations, seems to turn on 
thus dogmatically introducing information processing and then simply 
overlooking all alternative forms of computers and even alternative 



What Computers Can't Do / 98 

forms of digital data processing. This blindness to alternatives can be 
seen in the conclusion of Fodor's discussion of such phenomena as the 
recognition of melodies: 

Characteristically such phenomena have to do with "constancies" that is, cases 
in which normal perception involves radical and uniform departure from the 
informational content of the physical input. It has been recognized since Helm- 
holtz that such cases provide the best argument for unconscious mental opera- 
tions for there appears to be no alternative to invoking such operations if we are 
to explain the disparity between input and percept. 41 

Fodor's whole discussion of the logic of computer simulation is vi- 
tiated by his unquestioned reliance on these questionable assumptions. 
The ease with which his nonarguments pass for conceptual analysis 
reveals the grip of the Platonic tradition, and the need to believe in the 
information-processing level if psychology is to be a science. 

Of course, the use of the computer as a model is legitimate as long as 
it is recognized as an hypothesis. But in the writing of Miller et al., 
Neisser, and Fodor, as we have seen, this hypothesis is treated as an a 
priori truth, as if it were the result of a conceptual analysis of behavior. 

Occasionally one glimpses an empirical basis for this assumption: 
Fodor's argument for the legitimacy of a computer program as a psycho- 
logical theory ultimately rests on the hypothetical supposition "that we 
have a machine that satisfies whatever experimental tests we can devise 
for correspondences between its repertoire and that of some organism." 48 
However, this covertly empirical character of the argument is implicitly 
denied since the whole discussion is couched in terms of "sequences of 
mental operations," as if it were already certain that such a machine 
could exist. 

Only if such a machine existed, and only if it did indeed operate in 
sequences of steps, would one be justified in using the notions connected 
with heuristically programmed digital computers to suggest and inter- 
pret experiments in psychology. But to decide whether such an intelligent 
machine can exist, and therefore whether such a conceptual framework 
is legitimate, one must first try to program such a machine, or evaluate 
the programs already tried. To use computer language as a self-evident 



The Psychological Assumption / 99 

and unquestionable way of formulating the conceptional framework in 
terms of which experiments are undertaken and understood without 
valid a priori arguments or an empirical existence-proof of the possibility 
of such a machine, can only lead to confusion. 

Conclusion 

So we again find ourselves moving in a vicious circle. We saw at the end 
of Section I of this chapter that the empirical results, riddled with 
unexplained exceptions, and unable to simulate higher-order processes 
such as zeroing in and essential/inessential discrimination, are only 
promising if viewed in terms of an a priori assumption that the mind 
must work like a heuristically programmed digital computer. But now 
we have seen that the only legitimate argument for the assumption that 
the mind functions like a computer turns on the actual or possible 
existence of such an intelligent machine. 

The answer to the question whether man can make such a machine 
must rest on the evidence of work being done. And on the basis of actual 
achievements and current stagnation, the most plausible answer seems 
to be, No. It is impossible to process an indifferent "input" without 
distinguishing between relevant and irrelevant, significant and insignifi- 
cant data. We have seen how Newell, Shaw, and Simon have been able 
to avoid this problem only by predigesting the data, and how Miller et 
al. have been able to avoid it only by mistakenly supposing that Newell, 
Shaw, and Simon had a program which performed this original selection. 
But if there is no promising empirical evidence, the whole self-supporting 
argument tumbles down like a house of cards. 

The only alternative way to cope with selectivity would be analogue 
processing, corresponding to the selectivity of our sense organs. But then 
all processing would no longer be digital, and one would have reason to 
wonder whether this analogue processing was only peripheral. All of 
which would cast doubt on the "sequence of operations" and reopen the 
whole discussion. These difficulties suggest that, although man is surely 
a physical object processing physical inputs according to the laws of 



What Computers Can't Do / 700 

physics and chemistry, man's behavior may not be explainable in terms 
of an information-processing mechanism receiving and processing a set 
of discrete inputs. Moreover, nothing from physics or experience sug- 
gests that man's actions can be so explained, since on the physical level 
we are confronted with continuously changing patterns of energy, and 
on the phenomenological level with objects in an already organized field 
of experience. 

An analysis of this field of experience would provide an alternative 
area of study for psychology. But before we turn to this alternative the- 
ory in Part III, we must follow up two other assumptions, which, even 
if work in CS cannot be defended, seem to lend plausibility to work 
in AI. 



The Epistemological Assumption 



It should now be evident that it is extremely difficult to define what 
the mental level of functioning is, and that whatever the mind is, it is by 
no means obvious that it functions like a digital computer. This makes 
practically unintelligible the claims of those working in Cognitive Simu- 
lation that the mind can be understood as processing information accord- 
ing to heuristic rules. The computer model turns out not to be helpful 
in explaining what people actually do when they think and perceive, and, 
conversely, the fact that people do think and perceive can provide no 
grounds for optimism for those trying to reproduce human performance 
with digital computers. 

But this still leaves open another ground for optimism: although hu- 
man performance might not be explainable by supposing that people are 
actually following heuristic rules in a sequence of unconscious opera- 
tions, intelligent behavior may still be formalizable in terms of such rules 
and thus reproduced by machine. 1 * This is the epistemological assump- 
tion. 

Consider the planets. They are not solving differential equations as 
they swing around the sun. They are not following any rules at all; but 
their behavior is nonetheless lawful, and to understand their behavior we 
find a formalism in this case differential equations which expresses 
their behavior as motion according to a rule. Or, to take another example: 



What Computers Can't Do / 102 

A man riding a bicycle may be keeping his balance just by shifting his 
weight to compensate for his tendency to fall. The intelligible content of 
what he is doing, however, might be expressed according to the rule: 
wind along a series of curves, the curvature of which is inversely propor- 
tional to the square of the velocity. 2 * The bicycle rider is certainly not 
following this rule consciously, and there is no reason to suppose he is 
following it unconsciously. Yet this formalization enables us to express 
or understand his competence, that is, what he can accomplish. It is, 
however, in no way an explanation of his performance. It tells us what 
it is to ride a bicycle successfully, but nothing of what is going on in his 
brain or in his mind when he performs the task. 

There is thus a subtle but important difference between the psychologi- 
cal and the epistemological assumptions. Both assume the Platonic no- 
tion of understanding as formalization, but those who make the 
psychological assumption (those in CS) suppose that the rules used in the 
formalization of behavior are the very same rules which produce the 
behavior, while those who make the epistemological assumption (those 
in AI) only affirm that all nonarbitrary behavior can be formalized 
according to some rules, and that these rules, whatever they are, can then 
be used by a computer to reproduce the behavior. 

The epistemological assumption is weaker and thus less vulnerable 
than the psychological assumption. But it is vulnerable nonetheless. 
Those who fall back on the epistemological assumption have realized 
that their formalism, as a theory of competence, need not be a theory of 
human performance, but they have not freed themselves sufficiently 
from Plato to see that a theory of competence may not be adequate as 
a theory of machine performance either. Thus, the epistemological as- 
sumption involves two claims: (a) that all nonarbitrary behavior can be 
formalized, and (b) that the formalism can be used to reproduce the 
behavior in question. In this chapter we shall criticize claim (a) by 
showing that it is an unjustified generalization from physical science, and 
claim (b) by trying to show that a theory of competence cannot be a 
theory of performance: that unlike the technological application of the 
laws of physics to produce physical phenomena, a timeless, contextless 
theory of competence cannot be used to reproduce the moment-to- 



The Epistemological Assumption / 103 

moment involved behavior required for human performance; that indeed 
there cannot be a theory of human performance. If this argument is 
convincing, the epistemological assumption, in the form in which it 
seems to support AI, turns out to be untenable, and, correctly under- 
stood, argues against the possibility of AI, rather than guaranteeing its 
success. 

Claim (a), that all nonarbitrary behavior can be formalized, is not an 
axiom. It rather expresses a certain conception of understanding which 
is deeply rooted in our culture but may nonetheless turn out to be 
mistaken. We must now turn to the empirical arguments which can be 
given in support of such a hypothesis. It should also be clear by now that 
no empirical arguments from the success of AI are acceptable, since it 
is precisely the interpretation, and, above all, the possibility of significant 
extension of the meager results such as Bobrow's which is in question. 

Since two areas of successful formalization physics and linguistics 
seem to support the epistemological assumption, we shall have to study 
both these areas. In physics we indeed find a formalism which describes 
behavior (for example, the planets circling the sun), but we shall see that 
this sort of formalism can be of no help to those working in AI. In 
linguistics we shall find, on the other hand, a formalism which is relevant 
to work in AI, and which argues for the assumption that all nonarbitrary 
behavior can be formalized, but we will find that this formalism which 
expresses the competence of the speaker that is, what he is able to 
accomplish cannot enable one to use a computer to reproduce his 
performance that is, his accomplishment. 

I. A Mistaken Argument from the Success of 
Physics 

Minsky's optimism that is, his conviction that all nonarbitrary behav- 
ior can be formalized and the resulting formalism used by a digital 
computer to reproduce that behavior is a pure case of the epistemologi- 
cal assumption. It is this belief which allows Minsky to assert with 
confidence that "there is no reason to suppose that machines have any 
limitations not shared by man." 3 We must now examine the arguments 



What Computers Can't Do / 104 

supporting this claim, but first we must be clear what the formalist means 
by machine. 

A digital computer is a machine which operates according to the sort 
of criteria Plato once assumed could be used to understand any orderly 
behavior. This machine, as defined by Minsky, who bases his definition 
on that of Turing, is a "rule-obeying mechanism." As Turing puts it: 
"The . . . computer is supposed to be following fixed rules. ... It is the 
duty of the control to see that these instructions are obeyed correctly and 
in the right order. The control is so constructed that this necessarily 
happens." 4 So the machine in question is a restricted but very fundamen- 
tal sort of mechanism. It operates on determinate, unambiguous bits of 
data, according to strict rules which apply unequivocally to these data. 
The claim is made that this sort of machine a Turing machine which 
expresses the essence of a digital computer can, in principle, do anything 
that human beings can do that it has, in principle, only those limita- 
tions shared by man. 

Minsky considers the antiformalist counterclaim that "perhaps there 
are processes . . . which simply cannot be described in any formal 
language, but which can nevertheless be carried out, e.g., by minds." 5 
Rather than answer this objection directly, he refers to Turing's "bril- 
liant" article which, he asserts, contains arguments that "amount 
... to a satisfactory refutation of many such objections." 6 Turing does, 
indeed, take up this sort of objection. He states it as follows: "It is not 
possible to produce a set of rules purporting to describe what a man 
should do in every conceivable set of circumstances." 7 This is presuma- 
bly Turing's generalization of Wittgenstein's argument that it is impossi- 
ble to supply normative rules which prescribe in advance the correct use 
of a word in all situations. Turing's "refutation" is to make a distinction 
between "rules of conduct" and "laws of behavior" and then to assert 
that "we cannot so easily convince ourselves of the absence of complete 
laws of behavior as of complete rules of conduct." 8 

Now as an answer to the Wittgensteinian claim, this is well taken. 
Turing is in effect arguing that although we cannot formulate the norma- 
tive rules for the correct application of a particular predicate, this does 



The Epistemological Assumption / 705 

not show that we cannot formulate the rules which describe how, in fact. 
a particular individual applies such a predicate. In other words, while 
Turing is ready to admit that it may in principle be impossible to provide 
a set of rules describing what a person should do in every circumstance, 
he holds there is no reason to doubt that one could in principle discover 
a set of rules describing what he would do. But why does this supposition 
seem so self-evident that the burden of proof is on those who call it into 
question? Why should we have to "convince ourselves of the absence of 
complete laws of behavior" rather than of their presence? Here we are 
face to face again with the epistemological assumption. It is important 
to try to root out what lends this assumption its implied a priori plausi- 
bility. 

To begin with, "laws of behavior" is ambiguous. In one sense human 
behavior is certainly lawful, if lawful simply means orderly. But the 
assumption that the laws in question are the sort that could be embodied 
in a computer program or some equivalent formalism is a different and 
much stronger claim, in need of further justification. 

The idea that any description of behavior can be formalized in a way 
appropriate to computer programming leads workers in the field of 
artificial intelligence to overlook this question. It is assumed that, in 
principle at least, human behavior can be represented by a set of indepen- 
dent propositions describing the inputs to the organism, correlated with 
a set of propositions describing its outputs. The clearest statement of this 
assumption can be found in James Culbertson's move from the assertion 
that in theory at least one could build a robot using only flip/flops to the 
claim that it could therefore reproduce all human behavior. 

Using suitable receptors and effectors we can connect them together via central 
cells. If we could get enough central cells and if they were small enough and if 
each cell had enough endbulbs and if we could put enough bulbs at each synapse 
and if we had time enough to assemble them, then we could construct robots to 
satisfy any given input-output specification, i.e., we could construct robots that 
would behave in any way we desired under any environmental circumstances. 
There would be no difficulty in constructing a robot with behavioral properties 
just like John Jones or Henry Smith or in constructing a robot with any desired 
behavioral improvements over Jones and Smith. 9 



What Computers Can't Do / 106 

Or put more baldly: 

Since [these complete robots] can, in principle, satisfy any given input-output 
specifications, they can do any prescribed things under any prescribed circum- 
stances ingeniously solve problems, compose symphonies, create works of art 
and literature and engineering, and pursue any goals. 10 

But as we have seen in Chapter 4, it is not clear in the case of human 
beings what these inputs and outputs are supposed to be. 11 * Culbertson's 
assumption that the brain can be understood as correlating isolated bits 
of data rests on the assumption that the neurons act as on/off switches. 
Since, as we have seen in Chapter 3, this is probably not the case, there 
is no reason to suppose, and several reasons to doubt, that human inputs 
and outputs can be isolated and their correlation formalized. Culbert- 
son's assumption is an assumption and nothing more, and so in no way 
justifies his conclusions. 

The committed formalist, however, has one more move. He can ex- 
ploit the ambiguity of the notion of "laws of behavior," and take behavior 
to mean not meaningful human actions, but simply the physical move- 
ments of the human organism. Then, since human bodies are part of the 
physical world and, as we have seen, objects in the physical world have 
been shown to obey laws which can be expressed in a formalism manipu- 
lable on a digital computer, the formalist can still claim that there must 
be laws of human behavior of the sort required by his formalism. To be 
more specific, if the nervous system obeys the laws of physics and chemis- 
try, which we have every reason to suppose it does, then even if it is not 
a digital computer, and even if there is no input-output function directly 
describing the behavior of the human being, we still ought to be able to 
reproduce the behavior of the nervous system with some physical device 
which might, for example, take the form of a new sort of "analogue 
computer" using ion solutions whose electrical properties change with 
various local saturations. Then, as we pointed out in Chapter 4, knowing 
the composition of the solutions in this device would enable us at least 
in principle to write the physicochemical equations describing such wet 
components and to solve these equations on a dry digital computer. 



The Epistemological Assumption / 707 

Thus, given enough memory and time, any computer even such a 
special sort of analogue computer could be simulated on a digital ma- 
chine. In general, by accepting the fundamental assumptions that the 
nervous system is part of the physical world and that all physical pro- 
cesses can be described in a mathematical formalism which can in turn 
be manipulated by a digital computer, one can arrive at the strong claim 
that the behavior which results from human "information processing," 
whether directly formalizable or not, can always be indirectly repro- 
duced on a digital machine. 

This claim may well account for the formalist's smugness, but what 
in fact is justified by the fundamental truth that every form of "informa- 
tion processing" (even those which in practice can only be carried out 
on an "analogue computer") must in principle be simulable on a digital 
computer? We have seen it does not prove the mentalist claim that, even 
when a human being is unaware of using discrete operations in process- 
ing information, he must nonetheless be unconsciously following a set of 
instructions. Does it justify the epistemological assumption that all 
nonarbitrary behavior can be formalized? 

One must delimit what can count as information processing in a 
computer. A digital computer solving the equations describing an ana- 
logue information-processing device and thus simulating its function is 
not thereby simulating its "information processing." It is not processing 
the information which is processed by the simulated analogue, but en- 
tirely different information concerning the physical or chemical proper- 
ties of the analogue. Thus the strong claim that every form of information 
can be processed by a digital computer is misleading. One can only show 
that for any given type of information a digital computer can in principle 
be programmed to simulate a device which can process that information. 

Thus understood as motion as the input and output of physical 
signals human behavior is presumably completely lawful in the sense 
the formalists require. But this in no way supports the formalist assump- 
tion as it appears in Minsky and Turing. For when Minsky and Turing 
claim that man is a Turing machine, they cannot mean that a man is a 
physical system. Otherwise it would be appropriate to say that planes or 



What Computers Can't Do / 108 

boats are Turing machines. Their behavior, too, can be described by 
mathematically formulable laws relating their intake and output of 
energy and can at least in principle be reproduced to any degree of 
accuracy on a digital computer. No, when Minsky or Turing claims that 
man can be understood as a Turing machine, they must mean that a 
digital computer can reproduce human behavior, not by solving physical 
equations, but by processing data received from the world, by means of 
logical operations that can be reduced to matching, classifying, and 
Boolean operations. As Minsky puts it: 

Mental processes resemble ... the kinds of processes found in computer pro- 
grams: arbitrary symbol associations, treelike storage schemes, conditional trans- 
fers, and the like. 12 

Workers in AI are claiming that this mental level of "information pro- 
cessing" can be described in a digital formalism. All AI research is 
dedicated to using logical operations to manipulate data directly from 
the world, not to solving physical equations describing physical objects. 
Considerations from physics show only that inputs of energy, and the 
neurological activity involved in transforming them, can in principle be 
described in this digital form. 

No one has tried, or hopes to try, to use the laws of physics to calculate 
in detail the motion of human bodies. Indeed, this may well be physically 
impossible, for H. J. Bremermann has shown that: 

No data processing system whether artificial or living can process more than 
(2 X 10 47 ) bits per second per gram of its mass. 13 

Bremermann goes on to draw the following conclusions: 

There are\7T X 10 7 seconds in a year. The age of the earth is about 10 9 years, 
its mass less than 6 X 10 27 grams. Hence even a computer of the size of the earth 
could not process more than 10" bits during a time equal to the age of the earth. 
[Not to mention the fact, one might add, that the bigger the computer the more 
the speed of light would be a factor in slowing down its operation.] . . . Theorem 
proving and problem solving . . . lead to exponentially growing problem trees. 
If our conjecture is true then it seems that the difficulties that are currently 
encountered in the field of pattern recognition and theorem proving will not be 
resolved by sheer speed of data processing by some future super-computers. 14 



The Epistemological Assumption / 709 

If these calculations are correct, there is a special kind of impossibility 
involved in any attempt to simulate the brain as a physical system. The 
enormous calculations necessary may be precluded by the very laws of 
physics and information theory such calculations presuppose. 

Yet workers in the field of AI from Turing to Minsky seem to take 
refuge in this confusion between physical laws and information-process- 
ing rules to convince themselves that there is reason to suppose that 
human behavior can be formalized; that the burden of proof is on those 
who claim that "there are processes . . . which simply cannot be described 
in a formal language but which can nevertheless be carried out, e.g., by 
minds." 15 Once we have set straight the equivocation between physical 
laws and information-processing rules, what argument remains that hu- 
man behavior, at what AI workers have called "the information process- 
ing level," can be described in terms of strict rules? 

II. A Mistaken Argument from the Success of 
Modern Linguistics 

If no argument based on the success of physics is relevant to the success 
of AI, because AI is concerned with formalizing human behavior not 
physical motion, the only hope is to turn to areas of the behavioral 
sciences themselves. Galileo was able to found modern physics by ab- 
stracting from many of the properties and relations of Aristotelian phys- 
ics and finding that the mathematical relations which remained were 
sufficient to describe the motion of objects. What would be needed to 
justify the formalists' optimism would be a Galileo of the mind who, by 
making the right abstractions, could find a formalism which would be 
sufficient to describe human behavior. 

John McCarthy expresses this longing for a rapprochement between 
physics and the behavioral sciences: 

Although formalized theories have been devised to express the most important 
fields of mathematics and some progress has been made in formalizing certain 
empirical sciences, there is at present no formal theory in which one can express 
the kind of means-ends analysis used in ordinary life. . . . Our approach to the 
artificial-intelligence problem requires a formal theory. 16 



What Computers Can't Do / 110 

Recently such a breakthrough has occurred. Chomsky and the trans- 
formational linguists have found that by abstracting from human perfor- 
mance the use of particular sentences on particular occasions they 
can formalize what remains, that is, the human ability to recognize gram- 
matically well-formed sentences and to reject ill-formed ones. That is, 
they can provide a formal theory of much of linguistic competence. 11 * 
This success is a major source of encouragement for those in AI who are 
committed to the view that human behavior can be formalized without 
reduction to the physical level. For such success tends to confirm at least 
the first half of the epistemological hypothesis. A segment of orderly 
behavior which at first seems nonrulelike turns out to be describable in 
terms of complex rules, rules of the sort which can be processed directly 
by a digital computer (directly that is, without passing by way of a 
physical description of the motions of the vocal cords of a speaker or the 
physiochemical processes taking place in his brain). 

But such a formalization only provides justification for half the epis- 
temological hypothesis. Linguistic competence is not what AI workers 
wish to formalize. If machines are to communicate in natural language, 
their programs must not only incorporate the rules of grammar; they 
must also contain rules of linguistic performance. In other words, what 
was omitted in order to be able to formulate linguistic theory the fact 
that people are able to use their language is just what must also be 
formalized. 

The question whether the epistemological hypothesis is justified thus 
comes down to the test case: is there reason to suppose that there can 
be a formal theory of linguistic performance? There are two sorts of 
reasons to believe that such a generalization of linguistic theory is impos- 
sible: (1) An argument of principle (to which we shall turn in the next 
chapter): for there to be a theory of linguistic performance, one would 
have to have a theory of all human knowledge; but this, it seems, is 
impossible. (2) A descriptive objection (to which we shall now turn): not 
all linguistic behavior is rulelike. We recognize some linguistic expres- 
sions as odd as breaking the rules and yet we are able to understand 
them. 

There are cases in which a native speaker recognizes that a certain 



The Epistemological Assumption / 777 

linguistic usage is odd and yet is able to understand it for example, the 
phrase "The idea is in the pen" is clear in a situation in which we are 
discussing promising authors; but a machine at this point, with rules for 
what size physical objects can be in pig pens, playpens, and fountain 
pens, would not be able to go on. Since an idea is not a physical object, 
the machine could only deny that it could be in the pen or at best make 
an arbitrary stab at interpretation. The listener's understanding, on the 
other hand, is far from arbitrary. Knowing what he does about the 
shadow which often falls between human projects and their execution, 
as well as what one uses to write books, he gets the point, and the speaker 
will often agree on the basis of the listener's response that the listener 
has understood. Does it follow, then, that in understanding or using the 
odd utterance, the human speakers were acting according to a rule in 
this case a rule for how to modify the meaning of "in"? It certainly does 
not seem so to the speakers who have just recognized the utterance as 
"odd." 

This case takes us to the heart of a fundamental difficulty facing the 
simulators. Programmed behavior is either arbitrary or strictly rulelike. 
Therefore, in confronting a new usage a machine must either treat it as 
a clear case falling under the rules, or take a blind stab. A native speaker 
feels he has a third alternative. He can recognize the usage as odd, not 
falling under the rules, and yet he can make sense of it give it a meaning 
in the context of human life in an apparently nonrulelike and yet nonar- 
bitrary way. 

Outright misuse of language demonstrates an even more extreme form 
of this ability. People often understand each other even when one of the 
speakers makes a grammatical or semantic mistake. The utterance may 
not only be outside the rules but actually proscribed by them, and yet 
such violations often go unnoticed, so easily are they understood. 

Human beings confronted with these odd cases and outright errors 
adapt as they go along and then may reflect on the revisions they have 
made. A machine has either to fail first and then, when given the correct 
answer, revise its rules to take account of this new usage, or it would have 
to have all the rules even the rules for how to break the rules and still 
be understood built into it beforehand. To adopt the first approach, 



What Computers Can't Do / 772 

failing first and revising later, would be to admit that in principle, not 
just in practice, machines must always trail behind men that they could 
not be humanly intelligent. To assume, on the other hand, that the rules 
covering all cases must be explicitly built in or learned since this is the 
only way a digital computer could simulate the human ability to cope 
with odd uses runs counter to logic and experience. 

Logically, it is hard to see how one could formulate the rules for how 
one could intelligibly break the rules; for, no matter what metarules are 
formulated, it seems intuitively obvious that the native speaker could 
break them too and count on the context to get his meaning across to 
another speaker. Thus no matter what order of metarules one chooses, 
it seems there will be a higher order of tacit understanding about how 
to break those rules and still be understood. 

Phenomenologically, or empirically, the postulation of a set of uncon- 
scious metarules of which we are not aware leads to other difficulties. 
Just as in chess the acceptance of the digital model led to the assumption 
that the chess player must be using unconscious heuristics, even when 
the player reported that he was zeroing in on patterns of strength and 
weakness, the assumption of the pre-existence of rules for disambigua- 
tion introduces a process of which we have no experiential evidence, and 
fails to take seriously our sense of the oddness of certain uses. 

And here, as in the case of chess, this flouting of phenomenological 
evidence leads to a teleological puzzle: Why, if every understandable use 
of language is covered by rule, should some of these uses appear odd to 
us? So odd, indeed, that we cannot supply any rule to explain our 
interpretation. Why, if we have such a battery of rules and lightning-fast 
capacity for using them on the unconscious level, should we be left 
consciously perplexed in certain cases and find them peculiar even after 
we have understood them? 

These considerations suggest that, although a general theory of syntax 
and semantic competence can be scientific because it is a timeless for- 
malism which makes no claim to formalize the understanding of lan- 
guage in specific situations, contradictions arise when one demands a 
comparable formalism for linguistic use. 

These difficulties do not disturb the linguists who, like true scientists, 



The Epistemological Assumption / 113 

carefully limit themselves to linguistic competence, that is, the general 
principles which apply to all cases, and exclude as extralinguistic our 
ability to deal with specific situations as they occur. As Kierkegaard 
points out in his Concluding Unscientific Postscript, the laws of science 
are universal and timeless, treating all experience as if it could as well 
be in the past. 18 AI workers, however, want their machines to interact 
with people in present real-life situations in which objects have special 
local significance. But computers are not involved in a situation. Every 
bit of data always has the same value. True, computers are not what 
Kant would call "transcendentally stupid"; they can apply a rule to a 
specific case if the specific case is already unambiguously described in 
terms of general features mentioned in the rule. They can thus simulate 
one kind of theoretical understanding. But machines lack practical intel- 
ligence. They are "existentially" stupid in that they cannot cope with 
specific situations. Thus they cannot accept ambiguity and the breaking 
of rules until the rules for dealing with the deviations have been so 
completely specified that the ambiguity has disappeared. To overcome 
this disability, AI workers must develop an a-temporal, nonlocal, theory 
of ongoing human activity. 

The originality, the importance, and the curse of work in machine 
communication in a natural language is that the machine must use its 
formalism to cope with real-life situations as they occur. It must deal with 
phenomena which belong to the situational world of human beings as if 
these phenomena belonged to the objective formal universe of science. 
The believer in machine understanding and use of natural language who 
is encouraged by the success of linguistics is not laboring under a mis- 
conception about the way consciousness functions, but rather under a 
misconception about the relation between theoretical and practical 
understanding. He supposes that one can understand the practical world 
of an involved active individual in the same terms one can understand 
the objective universe of science. In short, he claims, as Leibniz first 
claimed, that one can have a theory of practice. 

But such an applied theory could not be the same as the technological 
application of a physical theory, which it seems to parallel. When one 
uses the laws of physics to guide missiles, for example, the present 



What Computers Can't Do / 114 

performance of the missile is an instantiation of timeless, universal laws 
which make no reference to the situation except in terms of such laws. 
But in linguistics, as we have seen, speakers using the language take for 
granted common situational assumptions and goals. Thus the general 
laws of competence cannot be directly applied to simulate behavior. To 
get from the linguistic formalism to specific performance, one has to take 
into account the speaker's understanding of his situation. If there could 
be an autonomous theory of performance, it would have to be an entirely 
new kind of theory, a theory for a local context which described this 
context entirely in universal yet nonphysical terms. Neither physics nor 
linguistics offers any precedent for such a theory, nor any comforting 
assurance that such a theory can be found. 

Conclusion 

But to refute the epistemological assumption that there must be a theory 
of practical activity in the case of language, to deny that the rules 
governing the use of actual utterances can in principle be completely 
formalized it is not sufficient to point out that thus far no adequate 
language translation system has been developed, or that our language is 
used in flexible and apparently nonrulelike ways. The formalizer can 
offer the Platonic retort that our failure to formalize our ability to use 
language shows only that we have not fully understood this behavior; 
have not yet found the rules for completely describing linguistic perfor- 
mance. 19 * 

This defense might at first seem to be similar to the heuristic program- 
mer's assurance that he will someday find the heuristics which will 
enable a machine to play chess, even if he has not yet found them. But 
there is an important difference. The heuristic programmer's confidence 
is based on an unfounded psychological assumption concerning the way 
the mind processes information, whereas the formalist's claim is based 
on a correct understanding of the nature of scientific explanation. To the 
extent that we have not specified our behavior in terms of unique and 
precisely defined reactions to precisely defined objects in universally 
defined situations, we have not understood that behavior in the only 



The Epistemological Assumption / 115 

sense of "understanding" appropriate to science. 

To answer this a priori claim of the theoretical understanding one 
cannot counter with a phenomenological description. One must show 
that the theoretical claim is untenable on its own terms: that the rules 
which enable a native speaker to speak cannot be completely formalized; 
that the epistemological assumption is not only implausible but leads to 
contradictions. 

Wittgenstein was perhaps the first philosopher since Pascal to note: 
"In general we don't use language according to strict rules it hasn't 
been taught us by means of strict rules either." 20 But Wittgenstein did 
not base his argument against the claim that language was a calculus 
solely on a phenomenological description of the nonrulelike use of lan- 
guage. His strongest argument is a dialectical one, based on a regress of 
rules. He assumes, like the intellectualist philosophers he is criticizing, 
that all nonarbitrary behavior must be rulelike, and then reduces this 
assumption to absurdity by asking for the rules which we use in applying 
the rules, and so forth. 

Here it is no longer a question of always being able to break the rules 
and still be understood. After all, we only feel we can go on breaking 
the rules indefinitely. We might be mistaken. It is a question of whether 
a complete understanding of behavior in terms of rules is intelligible. 
Wittgenstein is arguing, as Aristotle argued against Plato, that there 
must always be a place for interpretation. And this is not, as Turing 
seemed to think, merely a question of whether there are rules governing 
what we should do, which can legitimately be ignoreci. It is a question 
of whether there can be rules even describing what speakers in fact do. 
To have a complete theory of what speakers are able to do, one must not 
only have grammatical and semantic rules but further rules which would 
enable a person or a machine to recognize the context in which the rules 
must be applied. Thus there must be rules for recognizing the situation, 
the intentions of the speakers, and so forth. But if the theory then 
requires further rules in order to explain how these rules are applied, as 
the pure intellectualist viewpoint would suggest, we are in an infinite 
regress. Since we do manage to use language, this regress cannot be a 



What Computers Can't Do / 116 

problem for human beings. If AI is to be possible, it must also not be 
a problem for machines. 

Both Wittgenstein and the computer theorists must agree that there 
is some level at which the rules are simply applied and one no longer 
needs rules to guide their application. Wittgenstein and the AI theorists 
differ fundamentally, however, on how to describe this stopping point. 
For Wittgenstein there is no absolute stopping point; we just fill in as 
many rules as are necessary for the practical demands of the situation. 
At some level, depending on what we are trying to do, the interpretation 
of the rule is simply evident and the regress stops. 21 * 

For the computer people the regress also stops with an interpretation 
which is self-evident, but this interpretation has nothing to do with the 
demands of the situation. It cannot, for the computer is not in a situation. 
It generates no local context. The computer theorist's solution is to build 
the machine to respond to ultimate bits of context-free, completely deter- 
minate data which require no further interpretation in order to be under- 
stood. Once the data are in the machine, all processing must be rulelike, 
but in reading in the data there is a direct response to determinate 
features of the machine's environment as, for example, holes in cards or 
the mosaic of a TV camera, so on this ultimate level the machine does 
not need rules for applying its rules. Just as the feeding behavior of the 
baby herring gull is triggered by a red spot and the frog's eye automati- 
cally signals the presence of a moving black spot, so human behavior, if 
it is to be completely understood and computerized, must be understood 
as if triggered by specific features of the environment. 

As a theory of human psychology (CS) this is surely not a plausible 
hypothesis. Our sense of oddness of deviant linguistic uses, as well as our 
feeling that there is nothing in the environment to which we have an 
inevitable and invariable response, argue against this view. Moreover, as 
a theory of our "practical competence" (no matter how we actually 
produce our behavior), this hypothesis is no more attractive. The general 
adaptability of our language, which enables us to modify meanings and 
invent analogies, as well as the general flexibility of human and even 
higher animal behavior, are incomprehensible on this view. Still, these 
objections are all based on appearances. They are plausible, but not 



The Epistemological Assumption / 777 

necessarily convincing to those committed to the epistemological as- 
sumption. 

A full refutation of the epistemological assumption would require an 
argument that the world cannot be analyzed in terms of determinate 
data. Then, since the assumption that there are basic unambiguous ele- 
ments is the only way to save the epistemological assumption from the 
regress of rules, the formalist, caught between the impossibility of always 
having rules for the application of rules and the impossibility of finding 
ultimate unambiguous data, would have to abandon the epistemological 
assumption altogether. 

This assumption that the world can be exhaustively analyzed in terms 
of determinate data or atomic facts is the deepest assumption underlying 
work in AI and the whole philosophical tradition. We shall call it the 
ontological assumption, and now turn to analyzing its attraction and its 
difficulties. 



The Ontological Assumption 



Up to now we have been seeking in vain the arguments and evidence 
that the mind processes information in a sequence of discrete steps like 
a heuristically programmed digital computer, or that human behavior 
can be formalized in these terms. We have seen that there are four types 
of human information processing (fringe consciousness, ambiguity toler- 
ance, essential/inessential discrimination, and perspicuous grouping), 
which have resisted formalization in terms of heuristic rules. And we 
have seen that the biological, psychological, and epistemological assump- 
tions which allow workers to view these difficulties as temporary are 
totally unjustified and may well be untenable. Now we turn to an even 
more fundamental difficulty facing those who hope to use digital comput- 
ers to produce artificial intelligence: the data with which the computer 
must operate if it is to perceive, speak, and in general behave intelli- 
gently, must be discrete, explicit, and determinate; otherwise, it will not 
be the sort of information which can be given to the computer so as to 
be processed by rule. Yet there is no reason to suppose that such data 
about the human world are available to the computer and several reasons 
to suggest that no such data exist. 

The ontological assumption that everything essential to intelligent 
behavior must in principle be understandable in terms of a set of determi- 
nate independent elements allows AI researchers to overlook this prob- 



The Ontological Assumption / 779 

lem. We shall soon see that this assumption lies at the basis of all thinking 
in AI, and that it can seem so self-evident that it is never made explicit 
or questioned. As in the case of the epistemological assumption, we shall 
see that this conviction concerning the indubitability of what in fact is 
only an hypothesis reflects two thousand years of philosophical tradition 
reinforced by a misinterpretation of the success of the physical sciences. 
Once this hypothesis is made explicit and called into question, it turns 
out that no arguments have been brought forward in its defense and that, 
when used as the basis for a theory of practice such as AI, the ontological 
assumption leads to profound conceptual difficulties. 

In his introduction to Semantic Information Processing, Minsky warns 
against 

the dreadfully misleading set of concepts that people get when they are told (with 
the best intentions) that computers are nothing but assemblies of flip-flops; that 
their programs are really nothing but sequences of operations upon binary num- 
bers, and so on. 1 

He tries to combat this discouraging way of looking at digital computers: 

While this is one useful viewpoint, it is equally correct to say that the computer 
is nothing but an assembly of symbol-association and process-controlling ele- 
ments and that programs are nothing but networks of interlocking goal-formulat- 
ing and means-ends evaluation processes. This latter attitude is actually much 
healthier because it reduces one's egotistical tendency to assume total compre- 
hension of all the possible future implications. 2 

But Minsky sees only half the difficulty arising from the restriction 
that the computer must operate on determinate, independent elements. 
It may indeed be true that one can formulate higher-order rules for the 
operation of a computer so that the fact that there are flip/flops never 
appears in the flow chart, that is, on the information-processing level. 
(On this level, as we have seen in the preceding two chapters, trouble 
arises because there must always be explicit rules, not because these rules 
must ultimately be a sequence of operations on binary numbers.) The 
flip/flops only become a problem when we consider the kind of informa- 
tion the machine can be given. 3 * We have seen that Newell quite frankly 
described GPS a program whose information-processing level is cor- 



What Computers Can't Do / 120 

rectly described in terms of interlocking goals and means-ends as "a 
program for accepting a task environment defined in terms of discrete 
objects." 4 It is these discrete objects which must feed the flip/flops, or 
else must be analyzed into further discrete elements in order to do so. 
Every program for a digital computer must receive its data in this dis- 
crete form. 

This raises a special problem, or, more exactly, it creates a problem 
by determining the way all questions concerning giving information to 
computers must be raised. Stated in a neutral way the problem is this: 
as we have seen, in order to understand an utterance, structure a prob- 
lem, or recognize a pattern, a computer must select and interpret its data 
in terms of a context. But how are we to impart this context itself to the 
computer? The sharpest statement of this problem still in neutral terms 
occurs in Eden's evaluation of work in handwriting recognition: 

. . . when [a human being] reads a letter written in a difficult script ... he can 
reconstruct it with the help of his knowledge of the grammar of the language, 
the meaning of the text he has been able to read, the character of the subject 
matter, and, perhaps, the state of mind of the writer. There is now, alas, no hint 
of how to embody such knowledge of the world and its ways in the computer. 5 

Here Eden wisely takes no stand on what we know when we have 
"knowledge of the world and its ways." The flip/flops secretly come in, 
however, and, along with the ontological assumption, dictate an answer 
to this question which is no longer neutral, but rather embodies the 
computer's requirements. When one asks what this knowledge of the 
world is, the answer comes back that it must be a great mass of discrete 
facts. 

Thus at the end of his introduction to Semantic Information Process- 
ing, when Minsky finally asks "what is the magnitude of the mass of 
knowledge required for a humanoid intelligence?" 6 he has already pre- 
judged the question and unhesitatingly answers in terms of numbers of 
facts: 

If we discount specialized knowledge and ask instead about the common-every- 
day structures that which a person needs to have ordinary common sense we 
will find first a collection of indispensable categories, each rather complex: geo- 



The Ontological Assumption / 727 

metrical and mechanical properties of things and of space; uses and properties 
of a few thousand objects; hundreds of "facts" about hundreds of people, thou- 
sands of facts about tens of people, tens of facts about thousands of people; 
hundreds of facts about hundreds of organizations. As one tries to classify all his 
knowledge, the categories grow rapidly at first, but after a while one encounters 
more and more difficulty. My impression, for what it's worth, is that one can find 
fewer than ten areas each with more than ten thousand "links." One can't find 
a hundred things that he knows a thousand things about. Or a thousand things 
each with a full hundred new links. I therefore feel that a machine will quite 
critically need to acquire the order of a hundred thousand elements of knowledge 
in order to behave with reasonable sensibility in ordinary situations. A million, 
if properly organized, should be enough for a very great intelligence. If my 
argument does not convince you, multiply the figures by ten. 7 

Granting for the moment that all human knowledge can be analyzed 
as a list of objects and of facts about each, Minsky's analysis raises the 
problem of how such a large mass of facts is to be stored and accessed, 
How could one structure these data a hundred thousand discrete ele- 
ments so that one could find the information required in a reasonable 
amount of time? When one assumes that our knowledge of the world is 
knowledge of millions of discrete facts, the problem of artificial intelli- 
gence becomes the problem of storing and accessing a large data base. 
Minsky sees that this presents grave difficulties: 

... As everyone knows, it is hard to find a knowledge-classifying system that 
works well for many different kinds of problems: it requires immense effort to 
build a plausible thesaurus that works even within one field. Furthermore, any 
particular retrieval structure is liable to entail commitments making it difficult 
to incorporate concepts that appear after the original structure is assembled. One 
is tempted to say: "It would be folly to base our intelligent machine upon some 
particular elaborate, thesaurus-like classification of knowledge, some ad hoc 
synopticon. Surely that is no road to 'general intelligence.' " 8 

And, indeed, little progress has been made toward solving the large 
data base problem. But, in spite of his own excellent objections, Minsky 
characteristically concludes: 

But we had better be cautious about this caution itself, for it exposes us to a far 
more deadly temptation: to seek a fountain of pure intelligence. I see no reason 
to believe that intelligence can exist apart from a highly organized body of 



What Computers Can't Do / 722 

knowledge, models, and processes. The habit of our culture has always been to 
suppose that intelligence resides in some separated crystalline element, call it 
consciousness, apprehension, insight, gestalt, or what you will but this is merely 
to confound naming the problem with solving it. The problem-solving abilities 
of a highly intelligent person lies partly in his superior heuristics for managing 
his knowledge-structure and partly in the structure itself; these are probably 
somewhat inseparable. In any case, there is no reason to suppose that you can 
be intelligent except through the use of an adequate, particular, knowledge or 
model structure. 9 

But this is no argument for optimism. True, people manage to be 
intelligent, but without the ontological assumption this would be no 
consolation to workers in AI. It is by no means obvious that in order to 
be intelligent human beings have somehow solved or needed to solve the 
large data base problem. The problem may itself be an artifact created 
by the fact that the computer must operate with discrete elements. 
Human knowledge does not seem to be analyzable into simple categories 
as Minsky would like to believe. A mistake, a collision, an embarrassing 
situation, etc., do not seem on the face of it to be objects or facts about 
objects. Even a chair is not understandable in terms of any set of facts 
or "elements of knowledge." To recognize an object as a chair, for 
example, means to understand its relation to other objects and to human 
beings. This involves a whole context of human activity of which the 
shape of our body, the institution of furniture, the inevitability of fatigue, 
constitute only a small part. And these factors in turn are no more 
isolable than is the chair. They all may get their meaning in the context 
of human activity of which they form a part (see Chapter 8). 

In general, we have an implicit understanding of the human situation 
which provides the context in which we encounter specific facts and 
make them explicit. There is no reason, only an ontological commitment, 
which makes us suppose that all the facts we can make explicit about our 
situation are already unconsciously explicit in a "model structure," or 
that we could ever make our situation completely explicit even if we 
tried. 10 * 

Why does this assumption seem self-evident to Minsky? Why is he so 
unaware of the alternative that he takes the view that intelligence in- 



The Ontological Assumption / 723 

volves a "particular, knowledge or model structure," a great systematic 
array of facts, as an axiom rather than as an hypothesis? Ironically, 
Minsky supposes that in announcing this axiom he is combating the 
tradition. "The habit of our culture has always been to suppose that 
intelligence resides in some separated crystalline element, call it con- 
sciousness, apprehension, insight, gestalt. . . ." In fact, by supposing that 
the alternatives are either a well-structured body of facts, or some disem- 
bodied way of dealing with the facts, Minsky is so traditional that he 
can't even see the fundamental assumption that he shares with the whole 
of the philosophical tradition. In assuming that what is given are facts 
at all, Minsky is simply echoing a view which has been developing since 
Plato and has now become so ingrained as to seem self-evident. 

As we have seen, the goal of the philosophical tradition embedded in 
our culture is to eliminate all risk: moral, intellectual, and practical. 
Indeed, the demand that knowledge be expressed in terms of rules or 
definitions which can be applied without the risk of interpretation is 
already present in Plato, as is the belief in simple elements to which the 
rules apply. 11 * With Leibniz, the connection between the traditional idea 
of knowledge and the Minsky-like view that the world must be analyz- 
able into discrete elements becomes explicit. According to Leibniz, in 
understanding we analyze concepts into more simple elements. In order 
to avoid a regress of simpler and simpler elements, then, there must be 
ultimate simples in terms of which all complex concepts can be under- 
stood. Moreover, if concepts are to apply to the world, there must be 
logical simples to which these elements apply. Leibniz envisaged "a kind 
of alphabet of human thoughts" 12 whose "characters must show, when 
they are used in demonstrations, some kind of connection, grouping and 
order which are also found in the objects." 13 The empiricist tradition, too, 
is dominated by the idea of discrete elements of knowledge. For Hume, 
all experience is made up of impressions: isolable, determinate, atoms of 
experience. Intellectualist and empiricist schools converge in Russell's 
logical atomism, and the idea reaches its fullest expression in Wittgen- 
stein's Tractatus, where the world is defined in terms of a set of atomic 
facts which can be expressed in logically independent propositions. This 
is the purest formulation of the ontological assumption, and the neces- 



What Computers Can't Do / 124 

sary precondition of the possibility of AI, given the fact that digital 
computers, composed of flip/flops, must ultimately contain a model of 
the world represented as a structured set of facts or propositions which 
are either true or false. Thus both philosophy and technology finally posit 
what Plato sought: a world in which the possibility of clarity, certainty, 
and control is guaranteed; a world of data structures, decision theory, 
and automation. 

No sooner had this certainty finally been made fully explicit, how- 
ever, than philosophers began to call it into question. Continental 
phenomenologists recognized it as the outcome of the philosophical 
tradition and tried to show its limitations. Merleau-Ponty calls the as- 
sumption that all that exists can be treated as a set of atomic facts, the 
prejuge du monde, "presumption of commonsense." 14 Heidegger calls it 
rechnende Denken, I5 "calculating thought," and views it as the goal of 
philosophy, inevitably culminating in technology. Thus, for Heidegger, 
technology, with its insistence on the "thoroughgoing calculability of 
objects," 16 * is the inevitable culmination of metaphysics, the exclusive 
concern with beings (objects) and the concomitant exclusion of Being 
(very roughly our sense of the human situation which determines what 
is to count as an object). In England, Wittgenstein less prophetically and 
more analytically recognized the impossibility of carrying through the 
ontological analysis proposed in his Tractatus and became his own sever- 
est critic. 17 * 

In Part III, we shall have occasion to follow at length the Merleau- 
Pontyian, Wittgensteinian, and Heideggerian critique of the traditional 
ontological assumption, and the alternative view they propose. We have 
already seen enough, however, to suggest that we do not experience the 
world as a set of facts in our everyday activities, nor is it self-evident that 
it is possible to carry through such an analysis. 

But if the ontological assumption does not square with our experience, 
why does it have such power? Even if what gave impetus to the philo- 
sophical tradition was the demand that things be clear and simple so that 
we can understand and control them, if things are not so simple why 
persist in this optimism? What lends plausibility to this dream? As we 
have already seen in another connection, the myth is fostered by the 



The Ontologies! Assumption / 725 

success of modern physics. Here, at least to a first approximation, the 
ontological assumption works. It was only after Galileo was able to treat 
motion in terms of isolable objects moving under the influence of com- 
putable, determinate forces that Hobbes was encouraged to announce 
that all thinking was the addition of parcels. It has proved profitable to 
think of the physical universe as a set of independent interacting ele- 
ments. The ontological assumption that the human world too can be 
treated in terms of a set of elements gains plausibility when one fails to 
distinguish between world and universe, or what comes to the same 
thing, between the human situation and the state of a physical system. 
In Minsky's work this confusion remains implicit; in the work of his 
former colleague, John McCarthy, now directing AI research at Stan- 
ford, it becomes the very cornerstone of the argument. In his paper 
"Programs with Common Sense," included in the Minsky volume, 
McCarthy proposes an "advice taker" a program for "solving prob- 
lems by manipulating sentences in formal languages," the behavior of 
which "will be improvable merely by making statements to it, telling it 
about its symbolic environment and what is wanted from it." 18 McCarthy 
sees clearly that "the first requirement for the advice taker is a formal 
system in which facts about situation, goals, and actions can be ex- 
pressed." 19 This leads immediately to the basic problem: how can one 
describe the situation in a formal system? McCarthy, however, does not 
see this as a serious problem because he assumes without question that 
a situation is a physical state: 

One of the basic entities in our theory is the situation. Intuitively, a situation is 
the complete state of affairs at some instant in time. The laws of motion of a 
system determine all future situations from a given situation. Thus, a situation 
corresponds to the notion of a point in phase space. 20 

But the same type of situation can reoccur, involving different objects, 
different people, and a fortiori different physical states. Moreover, the 
same physical organization of matter can be seen as many different 
situations, depending on the goals and intentions of the various human 
beings involved. Thus, although at any given moment the universe is in 
only one physical state, there may be as many situations as there are 



What Computers Can't Do / 126 

people. When McCarthy says "there is only one situation corresponding 
to a given value of time,'* 21 he has clearly confused situation with physical 
state of the universe. More specifically, he has confused token states and 
types of states. A situation token can be identical with a physical state 
token (specified by a point in phase space). But a type of situation cannot 
be identical to a type of physical state. 

A concrete example will help to pinpoint this confusion. A situation 
which McCarthy discusses at length is "being at home.'* " 'At (I, home) 
(s)* means I am at home in situation s." 22 McCarthy seems to assume that 
this is the same thing as being in my house, that is, that it is a physical 
state. But I can be at home and be in the backyard, that is, not physically 
in my house at all. I can also be physically in my house and not be at 
home; for example, if I own the house but have not yet moved my 
furniture in. Being at home is a human situation, not in any simple 
correspondence with the physical state of a human body in a house. Not 
to mention the fact that it is a necessary if not sufficient condition for 
being at home in the sense in question that I own or rent the house, and 
owning or renting a house is a complicated institutional set of relations 
not reducible to any set of physical states. Even a physical description 
of a certain pattern of ink deposited on certain pieces of paper in a 
specific temporal sequence would not constitute a necessary and suffi- 
cient condition for a transfer of ownership. Writing one's name is not 
always signing, and watching is not always witnessing. 

It is easy to see why McCarthy would like to treat the situation as if 
it were a physical state. The evolution of a physical state can, indeed, be 
formalized in differential equations and reproduced on a digital com- 
puter. Situations, however, pose formidable problems for those who 
would like to translate them into a formal system. Such a formalization 
may well be impossible in principle, as can best be seen by returning to 
the problem of machine translation. 

We have seen in Part I that automatic language translation has failed 
because natural language turns out to be much more ambiguous than was 
supposed. In narrowing down this semantic and syntactic ambiguity the 
native speaker may appeal to specific information about the world. Bar- 
Hillel makes this point in an argument which according to him "amounts 



The Ontological Assumption / 727 

to an almost full-fledged demonstration of the unattainability of fully 
automatic high quality translation, not only in the near future but al- 
together." 23 The argument is sufficiently important at this point to merit 
quoting at some length. 

I shall show that there exist extremely simple sentences in English and the same 
holds, I am sure, for any other natural language which, within certain linguistic 
contexts, would be uniquely (up to plain synonymy) and unambiguously trans- 
lated into any other language by anyone with a sufficient knowledge of the two 
languages involved, though I know of no program that would enable a machine 
to come up with this unique rendering unless by a completely arbitrary and ad 
hoc procedure whose futility would show itself in the next example. 

A sentence of this kind is the following: 

The box was in the pen. 

The linguistic context from which this sentence is taken is, say, the following: 

Little John was looking for his toy box. Finally he found it. The box was in the 
pen. John was very happy. 

Assume, for simplicity's sake, that pen in English has only the following two 
meanings: (1) a certain writing utensil, (2) an enclosure where small children can 
play. I now claim that no existing or imaginable program will enable an elec- 
tronic computer to determine that the word pen in the given sentence within the 
given context has the second of the above meanings, whereas every reader with 
a sufficient knowledge of English will do this "automatically." 

What makes an intelligent human reader grasp this meaning so unhesitat- 
ingly is, in addition to all the other features that have been discussed by MT 
workers . . . , his knowledge that the relative sizes of pens, in the sense of writing 
implements, toy boxes, and pens, in the sense of playpens, are such that when 
someone writes under ordinary circumstances and in something like the given 
context, "The box was in the pen," he almost certainly refers to a playpen and 
most certainly not to a writing pen. 24 * 

And, as Bar-Hillel goes on to argue, the suggestion, such as Minsky's, 
that a computer used in translating be supplied with a universal ency- 
clopedia is "utterly chimerical." "The number of facts we human beings 
know is, in a certain very pregnant sense, infinite." 25 

Bar-HillePs point is well taken; his example, however, based on a 
particular physical fact, is unfortunate; it tempts AI workers such as 
Minsky to propose a solution in terms of a model of the facts of physics: 



What Computers Can't Do / 128 

". . . it would be a good idea to build into the semantic model enough 
common-sense geometrical physics to make it unlikely that the box is in 
the fountain-pen. . . ." 26 * 

There is a second kind of disambiguation, however, which gets us to 
the very heart of the difficulty. In disambiguating, one may appeal to a 
sense of the situation as in the following example from Katz and Fodor: 

An ambiguous sentence such as "He follows Marx" occurring in a setting in 
which it is clear that the speaker is remarking about intellectual history cannot 
bear the reading "he dogs the footsteps of Groucho." 27 

Katz and Fodor discuss this sort of difficulty in their article "The 
Structure of a Semantic Theory": 

Since a complete theory of setting selection must represent as part of the setting 
of an utterance any and every feature of the world which speakers need in order 
to determine the preferred reading of that utterance, and since . . . practically 
any item of information about the world is essential to some disambiguations, 
two conclusions follow. First, such a theory cannot in principle distinguish 
between the speaker's knowledge of his language and his knowledge of the world. 
. . . Second, since there is no serious possibility of systematizing all the knowledge 
about the world that speakers share . . . [such a theory] is not a serious model 
for linguistics. 25 

Katz and Fodor continue: 

None of these considerations is intended to rule out the possibility that, by 
placing relatively strong limitations on the information about the world that a 
theory can represent in the characterization of a setting, a limited theory of 
selection by sociophysical setting can be constructed. What these considerations 
do show is that a complete theory of this kind is not a possibility. 29 

Thus Bar-Hillel claims we must appeal to specific facts, such as the 
size of pens and boxes; Katz and Fodor assume we must appeal to the 
sociophysical setting. The appeal to context, would, moreover, seem to 
be more fundamental than the appeal to facts, for the context determines 
the significance of the facts. Thus in spite of our general knowledge about 
the relative size of pens and boxes, we might interpret "The box is in the 
pen," when whispered in a James Bond movie, as meaning just the 
opposite of what it means at home or on the farm. And, conversely, when 



The Ontological Assumption / 729 

no specifically odd context is specified, we assume a "normal" context 
and assign to the facts about relative size a "normal" significance. Min- 
sky's physical model hides but does not obviate the need for this implicit 
appeal to the situation. 

The important difference between disambiguation by facts and disam- 
biguation by appeal to the situation is not noted by Minsky, Bar-Hillel, 
or Fodor and Katz, presumably because they each assume that the 
setting is itself identified by features which are facts, and functions like 
a fact in disambiguation. We shall see, however, that disregarding the 
difference between fact and situation leads to an equivocation in both 
Bar-Hillel and Fodor-Katz as to whether mechanical translation is im- 
practical or impossible. 

In Bar-HilleFs "demonstration" that since disambiguation depends on 
the use of facts, and the number of facts is "in a certain very pregnant 
sense infinite," fully automatic high-quality mechanical translation is 
unattainable; it is unclear what is being claimed. If "unattainable" means 
that in terms of present computers, and programs in operation or en- 
visaged, no such massive storage and retrieval of information can be 
carried out, then the point is well made, and is sufficient to cast serious 
doubt on claims that mechanical translation has been achieved or can be 
achieved in the foreseeable future. But if "unattainable" means theoreti- 
cally impossible which the appeal to infinity seems to imply then 
Bar-Hillel is claiming too much. A machine would not have to store an 
infinite number of facts, for, as Minsky sees, from a large number of facts 
and rules for concatenating them, such as the laws of physics, it could 
produce further ones indefinitely. True, no present program would en- 
able a machine to sort through such an endless amount of data. At 
present there exist no machine and no program capable of storing even 
a very large body of data so as to gain access to the relevant information 
in manageable time. Still, there is work being done on what are called 
"associative memories" and ingenious tricks used in programming, such 
as hash coding, which may in the distant future provide the means of 
storing and accessing vast bodies of information. Then if all that was 
needed was facts, the necessary information might be stored in such a 



What Computers Can't Do / 130 

way that in any given case only a finite number of relevant facts need be 
considered. 

As long as Katz and Fodor, like Bar-Hillel, accept the ontological 
assumption and speak of the setting in terms of "items of information, 5 * 
their argument is as equivocal as his. They have no right to pass from 
the claim that there is "no serious possibility" of systematizing the 
knowledge necessary for disambiguation, which seems to be a statement 
about our technological capabilities, to the claim that a complete theory 
of selection by sociophysical setting is "not a possibility." If a program 
for handling all knowledge is ever developed, and in their world there 
is no theoretical reason why it should not be, it will be such a theory. 

Only if one rejects the ontological assumption that the world can be 
analyzed as a set of facts items of information can one legitimately 
move beyond practical impossibility. We have already seen examples 
which suggest that the situation might be of a radically different order 
and fulfill a totally different function than any concatenation of facts. In 
the "Marx" example, the situation (academic) determines how to disam- 
biguate "Marx" (Karl) and furthermore tells us which facts are relevant 
to disambiguate "follows," as ideological or chronological. (When was 
the follower born, what are his political views, etc.?) In the box-pen 
example the size of the box and pen are clearly relevant since we are 
speaking of physical objects being "in" other physical objects; but here 
the situation, be it agricultural, domestic, or conspiratorial, determines 
the significance of the facts involved. Thus it is our sense of the situation 
which enables us to select from the potential infinity of facts the immedi- 
ately relevant ones, and once these relevant facts are found, enables us 
to estimate their significance. This suggests that unless there are some 
facts whose relevance and significance are invariant in all situations 
and no one has come up with such facts we will have to give the 
computer a way of recognizing situations; otherwise, it will not be able 
to disambiguate and thus it will be, in principle, unable to understand 
utterances in a natural language. 

Among workers in AI, only Joseph Weizenbaum seems to be aware 
of these problems. In his work on a program which would allow people 
to converse with a computer in a natural language, Weizenbaum has had 



The Ontological Assumption / 131 

to face the importance of the situation, and realizes that it cannot be 
treated simply as a set of facts. His remarks on the importance of global 
context are worth quoting at length: 

No understanding is possible in the absence of an established global context. To 
be sure, strangers do meet, converse, and immediately understand one another. 
But they operate in a shared culture provided partially by the very language 
they speak and, under any but the most trivial circumstances, engage in a kind 
of hunting behavior which has as its object the creation of a contextual frame- 
work. 30 

In real conversation global context assigns meaning to what is being said in 
only the most general way. The conversation proceeds by establishing subcon- 
texts, sub-subcontexts within these, and so on. 31 

Weizenbaum sees difficulties in all this but no problems of principle. 

I call attention to the contextual matter ... to underline the thesis that, while 
a computer program that "understands*' natural language in the most general 
sense is for the present beyond our means, the granting of even a quite broad 
contextual framework allows us to construct practical language recognition 
procedures. 32 

Thus, Weizenbaum proposes to program a nest of contexts in terms 
of a "contextual tree": "beginning with the topmost or initial node, a new 
node representing a subcontext is generated, and from this one a new 
node still, and so on to many levels.'* 33 He clearly supposes these contexts 
can themselves ultimately be treated as sets of facts: "the analogue of a 
conversation tree is what the social psychologist Abelson calls a brief 
structure," 34 that is, an organized collection of facts concerning a per- 
son's knowledge, emotional attitudes, goals, and so forth. 

Evidently, an understanding of the crucial role of the situation does 
not by itself constitute a sufficient argument for abandoning AI. The 
traditional ontologist, reincarnated in Weizenbaum and every AI re- 
searcher, can grant that facts used in conversation are selected and 
interpreted in terms of the global context and simply conclude that we 
need only first pick out and program the features which identify this 
broader situation. But Weizenbaum's observations contain the elements 
of an objection in principle to the development of humanly intelligent 
machines. To see this we must first show that Weizenbaum's way of 



What Computers Can't Do / 132 

analyzing the problem separating the meaning of the context from the 
meaning of the words used in the context is not accidental but is 
dictated by the nature of a digital machine. In our everyday experience 
we do not find ourselves making such a separation. We seem to under- 
stand the situation in terms of the meaning of the words as much as we 
understand the meaning in terms of the situation. For a computer, 
however, this reciprocal determination must be broken down into a series 
of separate operations. Since Weizenbaum sees that we cannot determine 
the sense of the words until we know the meaning of the context, he 
correctly concludes, from a programmer's point of view, that we must 
first specify the context and then use this fixed context to determine the 
meaning of the elements in it. 

Moreover, Weizenbaum's analysis suggests that the computerized un- 
derstanding of a natural language requires that the contexts be organized 
as a nested hierarchy. To understand why Weizenbaum finds it necessary 
to use a hierarchy of contexts and work down from the top node, we must 
return to the general problem of situation recognition. If computers must 
utilize the situation or context in order to disambiguate, and in general 
to understand utterances in a natural language, the programmer must be 
able to program into the machine, which is not involved in a situation, 
a way of recognizing a context and using it. But the same two problems 
which arose in disambiguation and necessitated appeal to the situation 
in the first place arise again on the level of context recognition and force 
us to envisage working down from the broadest context: (1) If in disam- 
biguation the number of possibly relevant facts is in some sense infinite 
so that selection criteria must be applied before interpretation can begin, 
the number of facts that might be relevant to recognizing a context is 
infinite too. How is the computer to consider all the features such as how 
many people are present, the temperature, the pressure, the day of the 
week, and so forth, any one of which might be a defining feature of some 
context? (2) Even if the program provides rules for determining relevant 
facts, these facts would be ambiguous, that is, capable of defining several 
different contexts, until they were interpreted. 

Evidently, a broader context will have to be used to determine which 
of the infinity of features is relevant, and how each is to be understood. 



The Ontological Assumption / 133 

But if, in turn, the program must enable the machine to identify the 
broader context in terms of its relevant features and this is the only way 
a computer which operates in terms of discrete elements could proceed 
the programmer must either claim that some features are intrinsically 
relevant and have a fixed meaning regardless of context a possibility 
already excluded in the original appeal to context or the programmer 
will be faced with an infinite regress of contexts. There seems to be only 
one way out: rather than work up the tree to ever broader contexts the 
computer must work down from an ultimate context what Weizen- 
baum calls our shared culture. 

Fortunately, there does seem to be something like an ultimate context, 
but, as we shall see, this proves to be as unprogrammable as the regress 
it was introduced to avoid. We have seen that in order to identify which 
facts are relevant for recognizing an academic or a conspiratorial situa- 
tion, and to interpret these facts, one must appeal to a broader context. 
Thus it is only in the broader context of social intercourse that we see 
we must normally take into account what people are wearing and what 
they are doing, but not how many insects there are in the room or the 
cloud formations at noon or a minute later. Also only this broader 
context enables us to determine whether these facts will have their nor- 
mal significance. 

Moreover, even the facts necessary to recognize social intercourse can 
only be singled out because social intercourse is a subcase of human 
activity, which also includes working alone or studying a primitive tribe. 
And finally, human activity itself is only a subclass of some even broader 
situation call it the human life-world which would have to include 
even those situations where no human beings were directly involved. But 
what facts would be relevant to recognizing this broadest situation? Or 
does it make sense to speak of "recognizing" the life-world at all? It 
seems we simply take for granted this ultimate situation in being people. 
As Wittgenstein puts it: 

What has to be accepted, the given is someone could say forms of life." 

Well then, why not make explicit the significant features of the human 
form of life from within it? Indeed, this deus ex machina solution has 



What Computers Can't Do / 134 

been the implicit goal of philosophers for two thousand years, and it 
should be no surprise that nothing short of a formalization of the human 
form of life could give us artificial intelligence (which is not to say that 
this is what gives us normal intelligence). But how are we to proceed? 
Everything we experience in some way, immediate or remote, reflects our 
human concerns. Without some particular interest, without some partic- 
ular inquiry to help us select and interpret, we are back confronting the 
infinity of meaningless facts we were trying to avoid. 

It seems that given the artificial intelligence worker's conception of 
reason as calculation on facts, and his admission that which facts are 
relevant and significant is not just given but is context determined, his 
attempt to produce intelligent behavior leads to an antinomy. On the one 
hand, we have the thesis: there must always be a broader context; other- 
wise, we have no way to distinguish relevant from irrelevant facts. On 
the other hand, we have the antithesis: there must be an ultimate context, 
which requires no interpretation; otherwise, there will be an infinite 
regress of contexts, and we can never begin our formalization. 

Human beings seem to embody a third possibility which would offer 
a way out of this dilemma. Instead of a hierarchy of contexts, the present 
situation is recognized as a continuation or modification of the previous 
one. Thus we carry over from the immediate past a set of anticipations 
based on what was relevant and important a moment ago. This carryover 
gives us certain predispositions as to what is worth noticing. 

Programming this alternative, however, far from solving the problem 
of context recognition merely transforms a hierarchical regress into a 
temporal one. How does the situation which human beings carry along 
get started? To the programmer this becomes the question: how can we 
originally select from the infinity of facts those relevant to the human 
form of life so as to determine a context we can sequentially update? Here 
the answer seems to be: human beings are simply wired genetically as 
babies to respond to certain features of the environment such as nipples 
and smiles which are crucially important for survival. Programming 
these initial reflexes and letting the computer learn might be a way out 
of the context recognition problem; but it is important to note two 
reservations: no present work in artificial intelligence is devoted to this 



The Ontological Assumption / 135 

approach. 36 * In fact, artificial intelligence as it is now defined by Feigen- 
baum, Simon, Minsky, Weizenbaum, and others seems to be the attempt 
to produce fully formed adult intelligence, the way Athena sprang full 
grown from the head of Zeus. Moreover, it is by no means clear that the 
above proposal avoids the original dilemma. It leaves unexplained how 
the child develops from fixed responses elicited by fixed features of the 
environment, to the determination of meaning in terms of context which 
even AI workers agree characterizes the adult. 

Once the child can determine meanings in terms of the situation, the 
past situation can indeed be updated to arrive at the present one, but the 
original transition from fixed response to flexible response in terms of the 
meaning of the situation remains as obscure as before. Either the transi- 
tion must be understood as an ongoing modification of the previous 
situation, and we have assumed what was to be explained, or the so- 
called global context must be recognized in terms of fixed context-free 
features, and we have ignored the problem rather than solved it. Either 
the child or machine is able to select relevant facts, assign a normal 
significance to all relevant facts, and also to override this normal signifi- 
cance in an open-ended way and then no set of fixed features, not even 
the infant's, can be taken as having a fixed significance in terms of which 
to begin this process; or fixed features are all that is needed, but then we 
have to reject as illusory the very flexibility we were trying to explain. 
There seems to be no way to get into a situation and no way to recognize 
one from the outside. 

We nonetheless observe that generality and flexibility are developed 
gradually through learning, but now the whole problem is hidden in this 
learning process. The child seems at each moment to be either developing 
more complex fixed responses, or to have always already interpreted 
specific facts in terms of the overall context and to be gaining a more 
structured sense of the situation. If we reject the analysis in terms of fixed 
responses as inadequate because inapplicable to the adult, we are back 
facing a temporal version of the original antinomy. Either there must be 
a first context which a machine would not be able to recognize for want 
of a previous context in terms of which to single out its relevant features, 
or there will be a temporal regress of contexts extending infinitely into 



What Computers Can't Do / 136 

the past and the machine will not be able to begin the recognition 
process. 

As Kant noted, the resolution of an antinomy requires giving up the 
assumption that the two alternatives considered are the only possible 
ones. They are, indeed, the only alternatives open to someone trying to 
construct artificial reason. 37 * There must be another alternative, how- 
ever, since language is used and understood. There must be some way 
of avoiding the self-contradictory regress of contexts, or the incompre- 
hensible notion of recognizing an ultimate context, as the only way of 
giving significance to independent, neutral facts. The only way out seems 
to be to deny the separation of fact and situation, which we saw Weizen- 
baum was led to assume because of the serial procedure forced on him 
by the digital computer. If, as all agree, we are unable to eliminate the 
situation in favor of facts whose relevance and significance are fixed 
regardless of context, then the only alternative way of denying the sepa- 
ration of fact and situation is to give up the independence of the facts 
and understand them as a product of the situation. This would amount 
to arguing that only in terms of situationally determined relevance are 
there any facts at all. It also amounts to avoiding the problem of how 
to recognize the situation from outside by arguing that for an intelligence 
to have any facts to interpret, it must already be in a situation. 

Part III will show how this latter alternative is possible and how it is 
related to the rest of human life. Only then will it become clear why the 
fixed-feature alternative is empirically untenable, and also why the hu- 
man form of life cannot be programmed. 



Conclusion 



In surveying the four assumptions underlying the optimistic interpreta- 
tion of results in AI we have observed a recurrent pattern: In each case 
the assumption was taken to be self-evident an axiom seldom ar- 
ticulated and never called into question. In fact, the assumption turned 
out to be only one alternative hypothesis, and a questionable one at that. 
The biological assumption that the brain must function like a digital 
computer no longer fits the evidence. The others lead to conceptual 
difficulties. 

The psychological assumption that the mind must obey a heuristic 
program cannot be defended on empirical grounds, and a priori argu- 
ments in its defense fail to introduce a coherent level of discourse be- 
tween the physical and the phenomenological. This does not show that 
the task set for Cognitive Simulation is hopeless. However, this lack of 
defense of the psychological axiom does eliminate the only argument 
which suggested any particular reason for hope. If it could have been 
argued that information processing must proceed by heuristic rules, 
Cognitive Simulation would have had the promising task of finding these 
rules. Without the defense provided by this axiom, however, all difficul- 
ties besetting Cognitive Simulation research during the past ten years 
take on new significance; there is no reason to deny the growing body 
of evidence that human and mechanical information processing proceed 
in entirely different ways. 

7/37 



What Computers Can't Do / 138 

Researchers in AI (taking over from CS as Minsky has taken over 
from Simon) have written programs which allow the digital machine to 
approximate, by means of logical operations, the result which human 
beings seem to achieve by avoiding rather than resolving the difficulties 
inherent in formalization. But formalization of restricted contexts is an 
ad hoc "solution" which leaves untouched the problem of how to formal- 
ize the totality of human knowledge presupposed in intelligent behavior. 
This fundamental difficulty is hidden by the epistemological and ontolog- 
ical assumptions that all human behavior must be analyzable in terms 
of rules relating atomic facts. 

But the conceptual difficulties introduced by these assumptions are 
even more serious than those introduced by the psychological one. The 
inevitable appeal to these assumptions as a final basis for a theory of 
practice leads to a regress of more and more specific rules for applying 
rules or of more and more general contexts for recognizing contexts. In 
the face of these contradictions, it seems reasonable to claim that, on the 
information processing level, as opposed to the level of the laws of 
physics, we cannot analyze human behavior in terms of rule-governed 
manipulation of a set of elements. And since we have seen no argument 
brought forward by the AI theorists for the assumption that human 
behavior must be reproducible by a digital computer operating with 
strict rules on determinate bits, we would seem to have good philosoph- 
ical grounds for rejecting this assumption. 

If we do abandon all four assumptions, then the empirical data avail- 
able to date would take on different significance. It no longer seems 
obvious that one can introduce search heuristics which enable the speed 
and accuracy of computers to bludgeon through in those areas where 
human beings use more elegant techniques. Lacking any a priori basis 
for confidence, we can only turn to the empirical results obtained thus 
far. That brute force can succeed to some extent is demonstrated by the 
early work in the field. The present difficulties in game playing, language 
translation, problem solving, and pattern recognition, however, indicate 
a limit to our ability to substitute one kind of "information processing** 
for another. Only experimentation can determine the extent to which 
newer and faster machines, better programming languages, and cleverer 



Conclusion / 139 

heuristics can continue to push back the frontier. Nonetheless, the dra- 
matic slowdown in the fields we have considered and the general failure 
to fulfill earlier predictions suggest the boundary may be near. Without 
the four assumptions to fall back on, current stagnation should be 
grounds for pessimism. 

This, of course, has profound implications for our philosophical tradi- 
tion. If the persistent difficulties which have plagued all areas of artificial 
intelligence are reinterpreted as failures, these failures must be interpre- 
ted as empirical evidence against the psychological, epistemological, and 
ontological assumptions. In Heideggerian terms this is to say that if 
Western Metaphysics reaches its culmination in Cybernetics, the recent 
difficulties in artificial intelligence, rather than reflecting technological 
limitations, may reveal the limitations of technology. 



PART III 



ALTERNATIVES TO THE 



TRADITIONAL ASSUMPTIONS 



Introduction 



The psychological, epistemological, and ontological assumptions have 
this in common: they assume that man must be a device which calculates 
according to rules on data which take the form of atomic facts. Such a 
view is the tidal wave produced by the confluence of two powerful 
streams: first, the Platonic reduction of all reasoning to explicit rules and 
the world to atomic facts to which alone such rules could be applied 
without the risks of interpretation; second, the invention of the digital 
computer, a general-purpose information-processing device, which cal- 
culates according to explicit rules and takes in data in terms of atomic 
elements logically independent of one another. In some other culture, the 
digital computer would most likely have seemed an unpromising model 
for the creation of artificial reason, but in our tradition the computer 
seems to be the very paradigm of logical intelligence, merely awaiting the 
proper program to accede to man's essential attribute of rationality. 

The impetus gained by the mutual reinforcement of two thousand 
years of tradition and its product, the most powerful device ever invented 
by man, is simply too great to be arrested, deflected, or even fully 
understood. The most that can be hoped is that we become aware that 
the direction this impetus has taken, while unavoidable, is not the only 
possible direction; that the assumptions underlying the conviction that 
artificial reason is possible are assumptions, not axioms in short, that 



What Computers Can't Do / 144 

there may be an alternative way of understanding human reason which 
explains both why the computer paradigm is irresistible and why it must 
fail. 

Such an alternative view has many hurdles to overcome. The greatest 
of these is that it cannot be presented as an alternative scientific explana- 
tion. We have seen that what counts as "a complete description" or an 
explanation is determined by the very tradition to which we are seeking 
an alternative. We will not have understood an ability, such as the human 
mastery of a natural language, until we have found a theory, a formal 
system of rules, for describing this competence. We will not have under- 
stood behavior, such as the use of language, until we can specify that 
behavior in terms of unique and precisely definable reactions to precisely 
defined objects in universally defined situations. Thus, Western thought 
has already committed itself to what would count as an explanation of 
human behavior. It must be a theory of practice, which treats man as a 
device, an object responding to the influence of other objects, according 
to universal laws or rules. 

But it is just this sort of theory, which, after two thousand years of 
refinement, has become sufficiently problematic to be rejected by philoso- 
phers both in the Anglo-American tradition and on the Continent. It is 
just this theory which has run up against a stone wall in research in 
artificial intelligence. It is not some specific explanation, then, that has 
failed, but the whole conceptual framework which assumes that an expla- 
nation of human behavior can and must take the Platonic form, success- 
ful in physical explanation; that situations can be treated like physical 
states; that the human world can be treated like the physical universe. 
If this whole approach has failed, then in proposing an alternative ac- 
count we shall have to propose a different sort of explanation, a different 
sort of answer to the question "How does man produce intelligent behav- 
ior?" or even a different sort of question, for the notion of "producing" 
behavior instead of simply exhibiting it is already colored by the tradi- 
tion. For a product must be produced in some way; and if it isn't 
produced in some definite way, the only alternative seems to be that it 
is produced magically. 

There is a kind of answer to this question which is not committed 



Introduction / 145 

beforehand to finding the precise rulelike relations between precisely 
defined objects. It takes the form of a phenomenological description of 
the behavior involved. It, too, can give us understanding if it is able to 
find the general characteristics of such behavior: what, if any one thing, 
is involved in seeing a table or a house, or, more generally, in perception, 
problem solving, using a language, and so forth. Such an account can 
even be called an explanation if it goes further and tries to find the 
fundamental features of human activity which serve as the necessary and 
sufficient conditions for all forms of human behavior. 

Such an explanation owes a debt to Aristotle's method, although not 
to his arguments or descriptions. Whereas Plato sought rulelike criteria, 
Aristotle tried to describe the general structure of perception and judg- 
ment. But, as his notion that action is based on a practical syllogism 
shows, Aristotle still thought of man as a calculable and calculating sort 
of object a reckoning animal so that his actual descriptions are one 
step in the tradition which finally separated the rationality from the 
animality and tried to simulate the reckoning all by itself. 

It is only recently, now that the full implications of the attempt to treat 
man merely as an object or device have become apparent, that philoso- 
phers have begun to work out a new view. The pioneers were Heidegger 
and Wittgenstein. Since then many others, notably Maurice Merleau- 
Ponty and Michael Polanyi have, each on his own, applied, consolidated, 
and refined similar insights; and young thinkers such as Charles Taylor 
and Samuel Todes are continuing their research. In trying to lay out the 
alternative view that emerges when we confront the three basic assump- 
tions of the tradition with a phenomenological description of the struc- 
ture of human behavior, I shall be drawing on the work of all these men. 

I am fully aware that this "account" is vaguer and less experimental 
than that of either the behaviorists or intellectualists which it is meant 
to supplant. But one must not become so fascinated with the formalizable 
aspects of a subject that one forgets the significant questions which 
originally gave rise to the research, nor should one be so eager for 
experimental results that one continues to use old techniques just because 
they work, when they have ceased to lead to new insights. Chomsky is 
one of the few in the behavioral sciences who see this danger. 



What Computers Can't Do / 146 

Without wishing to exalt the cult of gentlemanly amateurism, one must neverthe- 
less recognize that the classical issues have a liveliness and significance that may 
be lacking in an area of investigation that is determined by the applicability of 
certain tools and methods, rather than by problems that are of intrinsic interest 
in themselves. 

The moral is not to abandon useful tools; rather, it is, first, that one should 
maintain enough perspective to be able to detect the arrival of that inevitable day 
when the research that can be conducted with these tools is no longer important; 
and, second, that one should value ideas and insights that are to the point, though 
perhaps premature and vague and not productive of research at a particular stage 
of technique and understanding. 1 

Taking this suggestion to heart, we shall explore three areas neces- 
sarily neglected in CS and AI but which seem to underlie all intelligent 
behavior: the role of the body in organizing and unifying our experience 
of objects, the role of the situation in providing a background against 
which behavior can be orderly without being rulelike, and finally the role 
of human purposes and needs in organizing the situation so that objects 
are recognized as relevant and accessible. 



The Role of the Body in Intelligent Behavior 



Adherents of the psychological and epistemological assumptions that 
human behavior must be formalizable in terms of a heuristic program 
for a digital computer are forced to develop a theory of intelligent behav- 
ior which makes no appeal to the fact that a man has a body, since at 
this stage at least the computer clearly hasn't one. In thinking that the 
body can be dispensed with, these thinkers again follow the tradition, 
which from Plato to Descartes has thought of the body as getting in the 
way of intelligence and reason, rather than being in any way indispens- 
able for it. If the body turns out to be indispensable for intelligent 
behavior, then we shall have to ask whether the body can be simulated 
on a heuristically programmed digital computer. If not, then the project 
of artificial intelligence is doomed from the start. These are the questions 
to which we must now turn. 

Descartes, the first to conceive the possibility of robots, was also the 
first to suggest the essential inadequacy of a finite state machine. He 
remarks in the Discourses: 

Although such machines could do many things as well as, or perhaps even better" 

than men, they would infallibly fail in certain others For while reason is a 

universal instrument which can be used in all sorts of situations, the organs of 
a machine have to be arranged in a particular way for each particular action. 
From this it follows that it is morally [i.e., practically] impossible that there 



What Computers Can't Do / 148 

should be enough different devices in a machine to make it behave in all the 
occurrences of life as our reason makes us behave. 1 

Thus, although not aware of the difference between a situation and a 
physical state, Descartes already saw that the mind can cope with an 
indefinite number of situations, whereas a machine has only a limited set 
of states and so will eventually reveal itself by its failure to respond 
appropriately. This intrinsic limitation of mechanism, Descartes claims, 
shows the necessity of presupposing an immaterial soul 

This is an interesting argument, and some version of it may indeed be 
valid, but it gets its plausibility from the assumption that a robot can be 
in only a relatively small number of states. When in a modern computer 
the number of possible states is of the order of 10 Iol , it is not clear just 
how much Descartes' objection proves. Such a machine could at least in 
principle respond to what would appear to be an indefinite number of 
situations. It would thus, on Descartes' view, be indistinguishable from 
a human being, destroying his argument that intelligent behavior is 
possible only if the mechanism behaving is somehow attached to a non- 
material soul. But one can raise a new objection, in some ways the exact 
opposite of Descartes'. A brain in a bottle or a digital computer might 
still not be able to respond to new sorts of situations because our ability 
to be in a situation might depend, not just on the flexibility of our nervous 
system, but rather on our ability to engage in practical activity. After 
some attempts to program such a machine, it might become apparent 
that what distinguishes persons from machines, no matter how cleverly 
constructed, is not a detached, universal, immaterial soul but an in- 
volved, self-moving, material body. 

Indeed, it is just the bodily side of intelligent behavior which has 
caused the most trouble for artificial intelligence. Simon, who has been 
only slightly daunted by the failures of the last ten years, now feels that 
"machines will be capable, within twenty years, of doing any work that 
a man can do," 2 but he admits: "Automation of a flexible central nervous 
system will be feasible long before automation of a comparatively flexible 
sensory, manipulative, or locomotive system." 3 But what if the work of 
the central nervous system depends on the locomotive system, or to put 



The Role of the Body in Intelligent Behavior / 149 

it phenomenologically, what if the "higher," determinate, logical, and 
detached forms of intelligence are necessarily derived from and guided 
by global and involved "lower" forms? Then Simon's optimism, based 
on the three assumptions underlying artificial intelligence and traditional 
philosophy, would be unjustified. 

The intractability of the "lower" functions has already produced a 
certain irony. Computer technology has been most successful in simulat- 
ing the so-called higher rational functions those which were once sup- 
posed to be uniquely human. Computers can deal brilliantly with ideal 
languages and abstract logical relations. It turns out that it is the sort 
of intelligence which we share with animals, such as pattern recognition 
(along with the use of language, which may indeed be uniquely human) 
that has resisted machine simulation. 

Let us reconsider two related areas in which work in artificial intelli- 
gence has not fulfilled early expectations: game playing and pattern 
recognition. Thus far I have tried to account for the failure by arguing 
that the task in question cannot be formalized, and by isolating the 
nonformal form of "information processing" necessarily involved. Now 
I shall try to show that the nonformalizable form of "information pro- 
cessing" in question is possible only for embodied beings. 

To make this clear we shall first have to consider human pattern 
recognition in more detail. With the aid of concepts borrowed from 
phenomenology, I shall try to show how pattern recognition requires a .. 
certain sort of indeterminate, global anticipation. This set or anticipation 
is characteristic of our body as a "machine" of nerves and muscles whose 
function can be studied by the anatomist, and also of our body as ex- 
perienced by us, as our power to move and manipulate objects in the 
world. I shall argue that a body in both these senses cannot be repro- 
duced by a heuristically programmed digital computer even one on 
wheels which can operate manipulators, and that, therefore, by virtue of 
being embodied, we can perform tasks beyond the capacities of any 
heuristically programmed robot. 

We have seen that the restricted applicability of pattern recognition 
programs suggests that human pattern recognition proceeds in some 



What Computers Can't Do / 150 

other way than searching through lists of traits. Indeed, phenomenolo- 
gists and Gestalt psychologists have pointed out that our recognition of 
ordinary spatial or temporal objects does not seem to operate by check- 
ing off a list of isolable, neutral, specific characteristics at all. For exam- 
ple, in recognizing a melody, the notes get their values by being perceived 
as part of the melody, rather than the melody's being recognized in terms 
of independently identified notes. Likewise, in the perception of objects 
there are no neutral traits. The same hazy layer which I would see as dust 
if I thought I was confronting a wax apple might appear as moisture if 
I thought I was seeing one that was fresh. The significance of the details 
and indeed their very look is determined by my perception of the whole. 

The recognition of spoken language offers the most striking demon- 
stration of this global character of our experience. From time to time 
brash predictions such as Rosenblatt's have been made about mechanical 
secretaries into which (or at whom) one could speak, and whose pro- 
grams would analyze the sounds into words and type out the results. In 
fact, no one knows how to begin to make such a versatile device, and 
further progress is unlikely, for current work has shown that the same 
physical constellation of sound waves is heard as quite different pho- 
nemes, depending on the expected meaning. 

Oettinger has given considerable attention to the problem. His analysis 
of speech recognition work is worth reproducing in detail, both because 
this pattern recognition problem is important in itself and because this 
work exhibits the early success and subsequent failure to generalize 
which we have come to recognize as typical of artificial intelligence 
research. 

There was considerable initial success in building apparatus that would eke out 
a sequence of discrete phonemes out of the continuous speech waveform. While 
phonemic analysis has been dominant in that area, numerous other approaches 
to this decoding problem have also been followed. All have shared this initial 
degree of success and yet all, so far, have proved to be incapable- of significant 
expansion beyond the recognition of the speech of a very few distinct individuals 
and the recognition of a very few distinct sound patterns whether they be pho- 
nemes or words or whatever. All is well as long as you are willing to have a fairly 
restricted universe of speakers, or sounds, or of both. 

Within these limitations you can play some very good tricks. There are now 
lots of machines, some experimental, some not so experimental, that will recog- 



The Role of the Body in Intelligent Behavior / 757 

nize somewhere between 20 and 100 distinct sound patterns, some of them quite 
elaborate. Usually the trick is something like identifying a number of features, 
treating these as if they were coordinates in some hyperspace, then passing planes 
that cordon off, if you will, different blocks of this space. If your speech event 
falls somewhere within one of these blocks you say that it must have been that 
sound and you recognize it. 

This game was fairly successful in the range of twenty to a hundred or so 
distinct things, but after that, these blocks become so small and clustered so close 
together that you no longer can achieve any reliable sort of separation. Every- 
thing goes to pot. 4 

This leads Oettinger to a very phenomenoJogical observation: 

Perhaps ... in perception as well as in conscious scholarly analysis, the phoneme 
comes after the fact, namely ... it is constructed, if at all, as a consequence of 
perception not as a step in the process of perception itself. 5 

This would mean that the total meaning of a sentence (or a melody or 
a perceptual object) determines the value to be assigned to the individual 
elements. 

Oettinger goes on reluctantly to draw this conclusion: 

This drives me to the unpopular and possibly unfruitful notion that maybe there 
is some kind of Gestalt perception going on, that here you are listening to me, 
and somehow the meaning of what Fm saying comes through to you all of a 
piece. And it is only a posteriori, and if you really give a damn, that you stop 
and say, "Now, here was a sentence and the words in it were of such and such 
type, and maybe here was a rfoun and here was a vowel and that vowel was this 
phoneme and the sentence is declarative, etc." 6 

Phenomenologists, not committed to breaking down the pattern so that 
it can be recognized by a digital computer, while less appalled, are no 
less fascinated by the gestalt character of perception. Indeed, it has been 
systematically studied in their account of perceptual horizons. Two 
forms of awareness are involved. First there is the basic figure-ground 
phenomenon, necessary for there to be any perception at all: whatever 
is prominent in our experience and engages our attention appears on a 
background which remains more or less indeterminate. This back- 
ground, which need never have been made determinate, affects the ap- 
pearance of what is determinate by letting it appear as a unified, bounded 
figure. In Rubin's famous "Peter-Paul Goblet" (Figure 3), "the contour 



What Computers Can't Do / 152 

which divides figure from ground 'belongs' to the figure only and changes 
its shape radically if a figure-ground reversal occurs." 7 Thus the figure 
has specific determinate characteristics, while the background can be 
characterized only as that-which-is-not-the figure. 




Figure 3 

This indeterminacy plays a crucial role in human perception. Merleau- 
Ponty points out that most of what we experience must remain in the 
background so that something can be perceived in the foreground. 

When Gestalt theory informs us that a figure on a background is the simplest 
sense-datum available to us, we reply that this is not a contingent characteriza- 
tion of factual perception, which leaves us free, in an ideal analysis, to bring in 
the notion of impression. It is the very definition of the phenomenon of percep- 
tion. . . . The perceptual 'something* is always in the middle of something else; 
it always forms part of a 'field.' 8 

It is this ground, or outer horizon as Edmund Husserl, the founder of 
phenomenology, called it, which in our chess example remains indeter- 
minate and yet provides the context of the specific counting out, so that 
one always has a sense of the relevance of the specific move under 



The Role of the Body in Intelligent Behavior / 753 

consideration to the rest of the game. Similarly, our sense of the overall 
context may organize and direct our perception of the details when we 
understand a sentence. For a computer, which must take up every bit of 
information explicitly or not at all, there could be no outer horizon. Any 
information to be taken into account would have to be as determinate 
as the figure. This leads to the unwieldy calculations which we have 
seen in chess programs and which Oettinger deplores in language pro- 
grams. 

This outer horizon, then, describes how background "information" 
about a conversation or a particular game is ignored without being 
excluded. It does not, however, describe the way the background pro- 
vides information which contributes to the player zeroing in on one area 
of the chess board rather than another, or how our anticipation of a 
sentence's meaning determines our understanding of its elements as they 
fall into place. To understand this, we must consider a second kind of 
perceptual indeterminacy investigated by Husserl and Gestalt psycholo- 
gists: what Husserl calls the inner horizon. The something-more-than- 
the-figure is, in this case, not as indeterminate as the outer horizon. 
When we perceive an object we are aware that it has more aspects than 
we are at the moment considering. Moreover, once we have experienced 
these further aspects, they will be experienced as copresent, as covered 
up by what is directly presented. Thus, in ordinary situations, we say we 
perceive the whole object, even its hidden aspects, because the concealed 
aspects directly affect our perception. We perceive a house, for example, 
as more than a facade as having some sort of back some inner hori- 
zon. We respond to this whole object first and then, as we get to know 
the object better, fill in the details as to inside and back. A machine with 
no equivalent of an inner horizon would have to process this information 
in the reverse order: from details to the whole. Given any aspect of an 
object, the machine would either pick it up on its receptors or it would 
not. All additional information about other aspects of the object would 
have to be explicitly stored in memory in Minsky's sort of model or 
counted out again when it was needed. This lack of horizons is the 
essential difference between an image in a movie or on a TV screen and 
the same scene as experienced by a human being. 



What Computers Can't Do / 154 

When, in a film, the camera is trained on an object and moves nearer to it to give 
a close-up view, we can remember that we are being shown the ash tray or an 
actor's hand, we do not actually identify it. This is because the scene has no 
horizons. 9 

In chess and in recognizing sentences, we find the same phenomenon 
playing a crucial role. Our sense of the whole situation, outer horizon, 
and our past experience with the specific object or pattern in question, 
inner horizon, give us a sense of the whole and guide us in filling in the 
details. 10 * 

This process can best be noticed when it is breaking down. If you reach 
for a glass of water and get milk by mistake, on taking a sip your first 
reaction is total disorientation. You don't taste water, but you don't taste 
milk either. You have a mouthful that approaches what Husserl would 
call pure sensuous matter or hyletic data, and naturally you want to spit 
it out. Or, if you find the right global meaning fast enough, you may 
recover in time to recognize the milk for what it is. Its other characteris- 
tics, whether it is fresh or sour, buttermilk or skimmed milk, will then 
fall into place. 

One might well wonder how one knows enough to try "milk" rathe'r 
than, say, "gasoline.*' Doesn't one need some neutral features to begin 
this process of recognition? The perceiver's apparent clairvoyance seems 
so paradoxical that one is tempted to embrace the computer model in 
spite of its difficulties. But the process seems less mysterious when we 
bear in mind that each new meaning is given in an outer horizon which 
is already organized, in this case a meal, on the basis of which we already 
have certain expectations. It is also important that we sometimes do give 
the wrong meaning; in these cases the data coming in make no sense at 
all, and we have to try a new total hypothesis. 

A computer, which must operate on completely determinate data 
according to strictly defined rules, could at best be programmed to try 
out a series of hypotheses to see which best fit the fixed data. But this 
is far from the flexible interaction of underdetermined data and underde- 
termmed expectations which seems to be characteristic of human pattern 
recognition. 

As one might expect, the computer people, again with the support of 



The Role of the Body in Intelligent Behavior / 755 

the philosophical tradition, and the success of physics, have rarely faced 
this problem. Philosophers have thought of man as a contemplative mind 
passively receiving data about the world and then ordering the elements. 
Physics has made this conception plausible on the level of the brain as 
a physical object. The brain does passively receive energy from the 
physical world and process it in terms of its present state which is a 
function of past energy received. If one accepts the passive view of mind 
and fails to distinguish the physical-processing level from the "informa- 
tion-processing" level, it seems self-evident that the mind, like the com- 
puter, simply receives bits of determinate data. In his introduction to the 
Scientific American issue on computers, McCarthy naively confuses 
brain and mind, energy and information, so that the passivity of the 
computer appears to be a self-evident model for human "information 
processing." 

The human brain also accepts inputs of information, combines it with informa- 
tion stored somehow within, and returns outputs of information to its environ- 
ment. 11 

. Neisser is much more subtle. He too underestimates the problems 
posed by the role of anticipation, but his work in psychology has at least 
led him to see the need for "wholistic operations which form the units 
to which attention may then be directed," 12 and he tries to fit this fact 
into his overall commitment to a digital computer model. The result is 
a confusion between what "global or wholistic" means in a gestalt analy- 
sis and what it would have to mean in a computer program, which is 
sufficiently revealing to be worth following in detail. 

A general characterization of the gestalt, or global, phenomenon is: 
the interpretation of a part depends on the whole in which it is embed- 
ded. But this is too general. Such a definition allows Minsky, for example, 
to miss the whole problem. In his Scientific American article he speaks 
of Evans' analogy-solving program as being able to "recognize a 'global' 
aspect of the situation." 13 This turns out to mean that, on the basis of 
calculations made on certain local features of a figure, the program 
segments two superimposed figures in one way rather than another. 
There is nothing here to surprise or interest those concerned with the 



What Computers Can't Do / 156 

way the gestalt, or global, configuration functions in our experience. 

To see the difference between the wholistic processes which interest 
Neisser and what Minsky calls global recognition, one needs a sharper 
characterization of the gestalt phenomenon. Neisser gives such a charac- 
terization in terms of a temporal gestalt, a rhythm (a favorite example 
of the Gestaltists): 

The parts (individual beats) get their meaning (relative position) from the whole, 
even though that whole does not exist at any moment of time. It exists, as one 
might say, in the subject's mind, as an intent . . . Gestalt. . . . u 

The crucial feature of this gestalt interpretation, that each part gets its 
meaning only in terms of the whole, is missing in Minsky's example, as 
it must be, since, as we have seen, for a digital computer, each complex 
whole must be constructed by the logical combination of independently 
defined elements. In Minsky's example, the elements already have a 
precise significance (or rather two possible precise significances), and it 
is simply a question of deciding which interpretation is appropriate in 
terms of a decision based on other determinate local features of the 
figure. 

Neisser's description of the "mind's intent" which gives the individual 
beats their significance, on the other hand, brings us to the center of the 
problem. The question is how the partially determinate anticipation, 
involved in game playing, pattern recognition, and intelligent behavior 
in general, can be simulated on a heuristically programmed digital com- 
puter so that a computer does not have to go through the enormous 
calculation required by an explicit internal model. More specifically for 
Neisser, the problem is how to reconcile his gestaltist analysis with a 
computer model of human performance. 

Neisser thinks he has a way. In discussing linguistic performance as 
an example of the gestalt eifect, Neisser thinks of the rules of grammar 
as the wholes into which the words fit as parts. 

The rules are structural That is, they do not dictate what particular words are 
to be used, but rather how they are to be related to each other and to the sentence 
as a whole. 15 



The Role of the Body in Intelligent Behavior / 757 

But this will not work. In the case of the rhythm, the whole determined 
the meaning of each element there is no such thing as a syncopated 
beat, for example, existing all by itself but for Neisser, in the case of 
language, the words already have a determinate set of possible meanings; 
the grammar simply provides a rule for selecting a meaning and combin- 
ing it with others. The elements in this case are completely determinate 
and can be defined independently of the rules. It is, therefore, misleading 
when Neisser concludes: r 

A sentence is more than the sum of its parts. This is not an unfamiliar slogan. 
Long ago, the Gestalt psychologists used it to describe the wholistic aspects of 
visual perception. 16 

This confusion is already latent in Neisser's description of the anticipa- 
tion involved in hearing a rhythm in the example quoted above. The 
description concludes: "[The anticipation] exists ... in the subject's mind 
as an intent, a gestalt, a plan, a description of a response that can be 
executed without further consideration." 11 This slide from gestalt antici- 
pation to preset plan is an obfuscation necessitated by the computer 
model: A gestalt determines the meaning of the elements it organizes; a 
plan or a rule simply organizes independently defined elements. More- 
over, just as the elements (the beats) cannot be defined independently of 
the gestalt, the gestalt (the rhythm) is nothing but the organization of 
the elements. A plan, on the other hand, can be stated as a rule or 
program, independently of the elements. Clearly his computer model of 
a formal program defined and stored separately from the independently 
defined bits of data which it organizes leads Neisser to betray his own 
gestaltist illustration. This difference is neglected in all CS models, yet 
it is the essence of the gestaltist insight, and accounts for the flexibility 
of human pattern recognition compared to that of machines. 

Thus far computer programs have been unable to approach this inter- 
dependence of parts and whole. Neisser himself never sees this problem, 
but he unwittingly casts some new light on the important differences 
between mechanist and gestaltist models of psychological processes 
when he contrasts the digital model of neural processes postulated by the 



What Computers Can't Do / 158 

transformational linguists with the analogue model of the brain espoused 
by the early Gestalt psychologists. 

[The Gestaltists] were "nativists," believing that the perceptual processes were 
determined by necessary and innate principles rather than by learning. The 

proper figural organization was due to processes in the brain, which followed 

unvarying (and wholistic) laws of physics and chemistry The perceived world 

always took the "best," the "structurally simplest" form, because of the equilib- 
rium principle that transcends any possible effects of learning or practice. 15 

Such an analogue model of brain function, in which information is 
integrated by equilibrium forces rather than on/off switches, was neces- 
sary if the Gestalt psychologists were to account for the role of global 
anticipations in structuring experience. They had been led to break with 
the rationalist tradition running from Descartes to Kant, which con- 
ceived of the mind as bringing independently defined innate principles 
(Descartes) or rules (Kant) to bear on otherwise unstructured experi- 
ence. This rationalist conception (with the addition of minimal bits of 
determinate experience) lends itself perfectly to a computer model, but 
the Gestaltists saw that their principles of organization like the equilib- 
rium patterns formed by charged particles on curved surfaces could 
not be separated from the elements they organized. Thus, even if the 
digital model of the brain had existed at the time, the Gestaltists would 
have rejected it. 19 * 

Neisser does not see this. He supposes that the digital model of built-in 
rules, which the linguists have been led to propose, is an improvement 
on the analogue model proposed by the Gestaltists. Neisser's praise of 
the linguists' "improvement," ignoring as it does the difficulties in artifi- 
cial intelligence, the latest developments in neurophysiology, and the 
reason the Gestaltists proposed an analogue model in the first place can 
only be a non sequitur: 

The Gestalt psychologists were never able to provide any satisfactory description 
or analysis of the structures involved in perception. The few attempts to specify 
"fields of force" in vision, or "ionic equilibria" in the brain, were ad hoc and 
ended in failure. In linguistics, by contrast, the study of "syntactic structures" 
has a long history. 20 



The Role of the Body in Intelligent Behavior / 759 

How the long history of syntactic structures is supposed to show that 
the linguists have a better model of neural processes than the Gestaltists 
is totally unclear. It seems to mean that at least the rules the linguists 
are looking for would be, if they were found, the sort of rules one could 
process with a digital computer which we already understand, whereas 
the gestaltist equilibrium principles could only be simulated on a brain- 
like analogue computer, which no one at present knows how to design. 

This is no doubt true, but it reminds one of the story of the drunk who 
lost a key in the dark but looked for it under a street lamp because the 
light was better. It would indeed be nice to have a programmable model 
in linguistics, and in psychology in general, but the fact remains that 
modern linguists have no more detailed account of what goes on in the 
brain than did the Gestaltists, and, moreover, as a theory of competence, 
not performance, modern linguistics is not even trying to provide an- 
swers to the problem of how we produce intelligent behavior. Worse, in 
this case, the street lamp is not even lit. We have seen that when digital 
computers have been used to try to simulate linguistic performance, they 
have had remarkably little success. 

The upshot of Neisser's comparison of gestalt and linguistic models of 
the brain, in opposition to his intent, is to call attention to a difference 
in brain model which exactly parallels the difference in the conception 
of the wholistic processes, which he also overlooks. The sort of gestalt 
process illustrated in Neisser's example of the rhythm which gives mean- 
ing to and is made up of its beats suggests that however the brain 
integrates stimuli, it does not do it like a digital computer applying 
independently defined heuristic rules to independently defined bits of 
data. 

Among computer experts only Donald MacKay has seen this point. 
He concludes: 

It may well be that only a special-purpose 'analogue* mechanism could meet ail 

detailed needs We on the circuit side had better be very cautious before we 

insist that the kind of information processing that a brain does can be replicated 
in a realizable circuit. Some kind of 'wet' engineering may turn out to be inevi- 
table. 21 



What Computers Can't Do / 160 

If, in the light of the phenomenological and neurophysiological evi- 
dence, we accept the view that the nervous system is some sort of 
analogue computer operating with equilibrium fields, we must still be on 
guard against transferring to psychology this model of the nervous sys- 
tem, conceived as a brain in a bottle receiving energy from the world and 
sending out responses. The human perceiver must be understood in 
different terms than his nervous system. To have an alternative account 
of intelligent behavior we must describe the general and fundamental 
features of human activity. In the absence of a workable digital computer 
model, and leaving to the neurophysiologist the question of how the 
brain integrates incoming physical stimuli, we must again ask, How do 
human beings use an underdetermined, wholistic expectation to organize 
their experience? 

Husserl has no further account beyond the assertion that we do: that 
"transcendental consciousness" has the ''wunderbar" capacity for giving 
meanings and thus making possible the perception, recognition, and 
exploration of enduring objects. Like the Gestaltists, he thinks of these 
meanings as partially indeterminate wholes, not as explicit programs or 
rules. But even Husserl is not free from the traditional intellectualist 
view, and thus he too is vulnerable to the criticism directed at Neisser. 
Husserl, like Descartes and Kant, thinks of form as separable from 
content, of the global anticipation as separable from its sensuous feeling. 
Thus, his noema, or perceptual anticipation, is like a rule or program in 
one crucial way: it exists in the mind or transcendental consciousness 
independently of its application to the experience it structures. 

Merleau-Ponty tries to correct HusserPs account on this point and at 
the same time develop a general description which supports the Gestalt- 
ists. He argues that it is the body which confers the meanings discovered 
by Husserl. After all, it is our body which captures a rhythm. We have 
a body-set to respond to the sound pattern. This body-set is not a rule 
in the mind which can be formulated or entertained apart from the actual 
activity of anticipating the beats. 

Generally, in acquiring a skill in learning to drive, dance, or pro- 
nounce a foreign language, for example at first we must slowly, awk- 
wardly, and consciously follow the rules. But then there comes a moment 
when we finally transfer control to the body. At this point we do not seem 



The Role of the Body in Intelligent Behavior / 767 

to be simply dropping these same rigid rules into unconsciousness; rather 
we seem to have picked up the muscular gestalt which gives our behavior 
a new flexibility and smoothness. The same holds for acquiring the skill 
of perception. To take one of Merleau-Ponty's examples: to learn to feel 
silk, one must learn to move or be prepared to move one's hand in a 
certain way and to have certain expectations. Before we acquire the 
appropriate skill, we experience only confused sensations. 

It is easiest to become aware of the body's role in taste, hearing, and 
touch, but seeing, too, is a skill that has to be learned. Focusing, getting 
the right perspective, picking out certain details, all involve coordinated 
actions and anticipations. As Piaget remarks, "Perceptual constancy 
seems to be the product of genuine actions, which consist of actual or 
potential movements of the glance or of the organs concerned. . . ." 22 

These bodily skills enable us not only to recognize objects in each 
single sense modality, but by virtue of the felt equivalence of our explora- 
tory skills we can see and touch the same object. A computer to do the 
same thing would have to be programmed to make a specific list of the 
characteristics of a visually analyzed object and compare that list to an 
explicit list of traits recorded by moving tactical receptors over that same 
object. This means that there would have to be an internal model of each 
object in each sense modality, and that the recognition of an object seen 
and felt must pass through the analysis of that object in terms of common 
features. 

My body enables me to by-pass this formal analysis. A skill, unlike a 
fixed response or set of responses can be brought to bear in an indefinite 
number of ways. When the percipient acquires a skill, he 

does not weld together individual movements and individual stimuli but acquires 
the power to respond with a certain type of solution to situations of a certain 
general form. The situations may differ widely from place to place, and the 
response movements may be entrusted sometimes to one operative organ, some- 
times to another, both situations and responses in the various cases having in 
common not so much a partial identity of elements as a shared significance. 23 

Thus I can recognize the resistance of a rough surface with my hands, 
with my feet, or even with my gaze. My body is thus what Merleau-Ponty 
calls a "synergic system,'* 24 "a ready-made system of equivalents and 
transpositions from one sense to another." 25 



What Computers Can't Do / 162 

Any object presented to one sense calls upon itself the concordant operation of 
all the others. I see a surface colour because I have a visual field, and because 
the arrangement of the field leads my gaze to that surface I perceive a thing 
because I have a field of existence and because each phenomenon, on its appear- 
ance, attracts towards that field the whole of my body as a system of perceptual 
powers. 26 

A human perceiver, like a machine, needs feedback to find out if he 
has successfully recognized an object. But here too there is an important 
difference in the feedback involved. A machine can, at best, make a 
specific set of hypotheses and then find out if they have been confirmed 
or refuted by the data. The body can constantly modify its expectations 
in terms of a more flexible criterion: as embodied, we need not check for 
specific characteristics or a specific range of characteristics, but simply 
for whether, on the basis of our expectations, we are coping with the 
object. Coping need not be defined by any specific set of traits but rather 
by an ongoing mastery which Merleau-Ponty calls maximum grasp. 
What counts as maximum grasp varies with the goal of the agent and 
the resources of the situation. Thus it cannot be expressed in situation- 
free, purpose-free terms. 

To conclude: Pattern recognition is relatively easy for digital comput- 
ers if there are a few specific traits which define the pattern, but complex 
pattern recognition has proved intractable using these methods. Tran- 
scendental phenomenologists such as Husserl have pointed out that 
human beings recognize complex patterns by projecting a somewhat 
indeterminate whole which is progressively filled in by anticipated ex- 
periences. Existential phenomenologists such as Merleau-Ponty have 
related this ability to our active, organically interconnected body, set to 
respond to its environment in terms of a continual sense of its own 
functioning and goals. 

Since it turns out that pattern recognition is a bodily skill basic to all 
intelligent behavior, the question of whether artificial intelligence is pos- 
sible boils down to the question of whether there can be an artificial 
embodied agent. The question is philosophically interesting only if we 
restrict ourselves to asking if one can make such a robot by using a digital 
computer. (I assume there is no reason why, in principle, one could not 



The Role of the Body in intelligent Behavior / 163 

construct an artificial embodied agent if one used components sufficiently 
like those which make up a human being.) 

A project to build such a digitally controlled robot is currently under 
way at M.I.T., and it is philosophically interesting to consider its pro- 
gram and its underlying assumptions. The project director, Minsky 
again, is modestly trying to make only a mechanical shoulder, arm, and 
hand, coordinated with a TV eye, but he proposes to make it use tools 
to construct things. The first simple task was to program a simplified 
robot arm to pick up blocks. This has indeed been accomplished and 
represents the early success one has learned to expect in the field. The 
problem which remains is, as usual, that of generalizing the present 
successful techniques. To bring a simple arm over to pick up a block 
requires locating the block in objective space, locating the arm in the 
same space, and then bringing the two together. This is already quite a 
feat. A mathematical description of the way an arm moves in objective 
space runs into surprising discontinuities. There are points which are 
contiguous in objective space which are far apart in reaching space. For 
example, to scratch our back we do not simply extend the position we 
use for scratching our ear. Living in our bodies we have built up a motor 
space, in which we sense these objectively contiguous points as far apart. 
We automatically reach for them in very different ways, and do not feel 
we have gone through the mathematics necessary to work out the opti- 
mal path for each specific case. For the programmer, however, who has 
to program the computer to calculate the movements of the mechanical 
arm in objective space, these discontinuities have so far proved an insur- 
mountable obstacle. The more flexible the arm the more degrees of 
freedom it has the more difficult and time consuming such calculations 
become. Rumor has it that an elaborate arm with six degrees of freedom, 
built by Minsky by 1965, has still not even been programmed to move, 
let alone pick up blocks or use tools. If one adds to this the fact that, in 
the case of any skill which takes place in real time (such as playing 
Ping-Pong), all calculations must be completed in real time (before the 
ball arrives), the outlook is not very promising. As Feigenbaum notes in 
his report on the current state of robot work: 



What Computers Can't Do / 164 

Both the MIT and Stanford University groups have worked on programs for 
controlling a variety of arm-hand manipulators, from the very simple to the very 
complex, from the anthropomorphic variety to the very non-anthropomorphic. 
None of the more esoteric manipulators seems to have worked out very well, 
though there is no published documentation of successes, failures, and rea- 
sons. 27 

In the light of these difficulties, what encourages researchers to devote 
their research facilities to such a project? Simply the conviction that since 
we are, as Minsky ingenuously puts it, "meat machines" and are able to 
play ping-pong, there is no reason in principle or in practice why a metal 
machine cannot do likewise. But before jumping to such a conclusion, 
the robot makers ought first to examine their underlying assumption that 
no essential difference exists between meat machines and metal ma- 
chines, between being embodied and controlling movable manipulators. 
How do human beings play ping-pong, or to make the matter simpler, 
how do human beings use tools? 

Heidegger, Merleau-Ponty, and Michael Polanyi have each devoted a 
great deal of thought to this question. Each discusses the important way 
that our experience of a tool we are using differs from our experience of 
an object. A blind man who runs his hand along the cane he uses to grope 
his way will be aware of its position and its objective characteristics such 
as weight, hardness, smoothness, and so forth. When he is using it, 
however, he is not aware of its objective position, its physical traits, nor 
of the varying pressure in the palm of his hand. Rather, the stick has 
become, like his body, a transparent access to the objects he touches with 
it. As Polanyi puts it: 

While we rely on a tool or a probe, these are not handled as external objects 
. . . they remain on our side . . . forming part of ourselves, the operating persons. 
We pour ourselves out into them and assimilate them as parts of our existence. 
We accept them existentially by dwelling in them. 28 

In this way we are able to bring the probe into contact with an object 
in physical space without needing to be aware of the physical location 
of the probe. Merleau-Ponty notes that: 

The whole operation takes place in the domain of the phenomenal; it does not 
run through the objective world, and only the spectator, who lends his objective 



The Role of the Body in Intelligent Behavior / 165 

representation of the living body to the active subject, can believe that . . . the 
hand moves in objective space. 29 

But Merleau-Ponty admits that this ability seems "magical" from the 
point of view of science, so we should not be surprised to find that rather 
than have no explanation of what people are able to do, the computer 
scientist embraces the assumption that people are unconsciously running 
with incredible speed through the enormous calculation which would be 
involved in programming a computer to perform a similar task. However 
implausible, this view gains persuasiveness from the absence of an alter- 
native account. 

To make embodiment an acceptable alternative we will have to show 
how one could perform physical tasks without in any way appealing to 
the principles of physics or geometry. Consider the act of randomly 
waving my hand in the air. I am not trying to place my objective hand 
at an objective point in space. To perform this waving I need not take 
into account geometry, since I am not attempting any specific achieve- 
ment. Mow suppose that, in this random thrashing about, I happen to 
touch something, and that this satisfies a need to cope with things. (More 
about need in Chapter 9.) I can then repeat whatever I did this time 
in order to touch something without appealing to the laws necessary 
to describe my movement as a physical motion. I now have a way of 
bringing two objects together in objective space without appealing to any 
principle except: "Do that again." This is presumably the way skills are 
built up. The important thing about skills is that, although science 
requires that the skilled performance be described according to rules, 
these rules need in no way be involved in producing the performance. 

Human beings are further capable of remembering, refining, and reor- 
ganizing these somewhat indeterminate motor schemata. Piaget has 
amassed an enormous amount of evidence tracing the development of 
these motor skills, which he calls operations, and has come to a gestaltist 
conclusion: 

The specific nature of operations . . . depends on the fact that they never exist 

in a discontinuous state A single operation could not be an operation because 

the peculiarity of operations is that they form systems. Here we may well protest 
vigorously against logical atomism ... a grievous hindrance to the psychology 
of thought. 30 * 



What Computers Can't Do / 166 

This same analysis helps dissipate the mistaken assumptions underly- 
ing early optimism about language translation. If human beings had to 
apply semantic and syntactic rules and to store and access an infinity of 
facts in order to understand a language, they would have as much trouble 
as machines. The native speaker, however, is not aware of having gener- 
ated multiple semantic ambiguities which he then resolved by appeal to 
facts any more than he is aware of having picked out complex patterns 
by their traits or of having gone through the calculations necessary to 
describe the way he brings his hand to a certain point in objective space. 
Perhaps language, too, is a skill acquired by innately guided thrashing 
around and is used in a nonrulelike way. Wittgenstein suggests this point 
when he notes, "In general we don't use language according to strict 
rules it hasn't been taught us by means of strict rules either." 31 

Such a view is not behavioristic. Our ability to use language in a 
situation and in general the wholistic way the functional meaning organ- 
izes and structures the components of skilled acts cannot be accounted 
for in terms of the arbitrary association of neutral determinate elements 
any more than it can be analyzed in terms of their combination according 
to rules. 

If language is understood as a motor skill, we would then assimilate 
language and dwell in it the way we assimilate an instrument. As Polanyi 
puts it, 

To use language in speech, reading and writing, is to extend our bodily equip- 
ment and become intelligent human beings. We may say that when we learn to 
use language, or a probe, or a tool, and thus make ourselves aware of these things 
as we are of our body, we interiorise these things and make ourselves dwell in 
them.'-* 

Again, because we are embodied, the rules necessary to give an objective 
analysis of our competence need in no way be involved in our perfor- 
mance. 

The AI researcher and the transcendental phenomenologist share the 
assumption that there is only one way to deal with information: it must 
be made an object for a disembodied processor. For the transcendental 



The Role of the Body in Intelligent Behavior / 167 

phenomenologist this assumption makes the organization of our intelli- 
gent behavior unintelligible. For the AI researcher it seems to justify the 
assumption that intelligent behavior can be produced by passively receiv- 
ing data and then running through the calculations necessary to describe 
the objective competence. But, as we have seen, being embodied creates 
a second possibility. The body contributes three functions not present, 
and not as yet conceived in digital computer programs: (1) the inner 
horizon, that is, the partially indeterminate, predelineated anticipation 
of partially indeterminate data (this does not mean the anticipation of 
some completely determinate alternatives, or the anticipation of com- 
pletely unspecified alternatives, which would be the only possible digital 
implementation); (2) the global character of this anticipation which 
determines the meaning of the details it assimilates and is determined by 
them; (3) the transferability of this anticipation from one sense modality 
and one organ of action to another. All these are included in the general 
human ability to acquire bodily skills. Thanks to this fundamental ability 
an embodied agent can dwell in the world in such a way as to avoid the 
infinite task of formalizing everything. 

This embodied sort of "information processing," in which the meaning 
of the whole is prior to the elements, would seem to be at work in the 
sort of complex pattern recognition such as speech recognition with 
which we began our discussion. It is also necessary, in order to account 
for our ability to recognize typicality, family resemblances, and 
similarity, where the objects recognized need have no traits in common 
at all. In all these cases individual features get their significance in terms 
of an underdetermined anticipation of the whole. 

If these global forms of pattern recognition are not open to the digital 
computer, which, lacking a body, cannot respond as a whole, but must 
build up its recognition starting with determinate details, then Oettinger 
is justified in concluding his speech recognition paper on a pessimistic 
note: "If indeed we have an ability to use a global context without 
recourse to formalization . . . then our optimistic discrete enumerative 
approach is doomed. . . ," 33 



8 



The Situation: Orderly Behavior Without Recourse 
to Rules 



In discussing problem solving and language translation we have come 
up against the threat of a regress of rules for determining relevance and 
significance. Likewise, in starting a learning process, something must be 
known before any rules can be taught or applied. In each case we have 
found that if there are no facts with fixed significance, only an appeal to 
the context can bring this regress to a halt. We must now turn directly 
to a description of the situation or context in order to give a fuller 
account of the unique way human beings are "in-the- world," and the 
special function this world serves in making orderly but nonrulelike 
behavior possible. 

To focus on this question it helps to bear in mind the opposing posi- 
tion. In discussing the epistemological assumption (Chapter 5) we saw 
that our philosophical tradition has come to assume that whatever is 
orderly can be formalized in terms of rules. This view has reached its 
most striking and dogmatic culmination in the conviction of AI workers 
that every form of intelligent behavior can be formalized. Minsky has 
even developed this dogma into a ridiculous but revealing theory of 
human free will. He is convinced that all regularities are rule governed. 
He therefore theorizes that our behavior is either completely arbitrary 
or it is regular and completely determined by rules. As he puts it: 

/ 168 



Orderly Behavior Without Recourse to Rules / 169 

". . . whenever a regularity is observed [in our behavior], its representa- 
tion is transferred to the deterministic rule region." 1 Otherwise our 
behavior is completely arbitrary and free. The possibility that our behav- 
ior might be regular but not rule governed never even enters his mind. 

We shall now try to show not only that human behavior can be regular 
without being governed by formalizable rules, but, further, that it has to 
be, because a total system of rules whose application to all possible 
eventualities is determined in advance makes no sense. 

In our earlier discussion of problem solving we restricted ourselves to 
formal problems in which the subject had to manipulate unambiguous 
symbols according to a given set of rules, and to other context-free 
problems such as analogy intelligence tests. But if CS is to provide a 
psychological theory and if AI programs are to count as intelligent 
they must extend mechanical information processing to all areas of 
human activity, even those areas in which people confront and solve 
open-structured problems in the course of their everyday lives. 2 * 

Open-structured problems, unlike games and tests, raise three sorts of 
difficulties: one must determine which facts are possibly relevant; which 
are actually relevant; and, among these, which are essential and which 
inessential. To begin with, in a given situation not all facts fall within the 
realm of possible relevancy. They do not even enter the situation. Thus, 
in the context of a game of chess, the weight of the pieces is irrelevant. 
It can never come into question, let alone be essential or inessential for 
deciding on a specific move. In general, deciding whether certain facts 
are relevant or irrelevant, essential or inessential, is not like taking blocks 
out of a pile and leaving others behind. What counts as essential depends 
on what counts as inessential and vice versa, and the distinction cannot 
be decided in advance, independently of some particular problem, or 
some particular stage of some particular game. Now, since facts are not 
relevant or irrelevant in a fixed way, but only in terms of human pur- 
poses, all facts are possibly relevant in some situation. Thus for example, 
if one is manufacturing chess sets, the weight is possibly relevant (al- 
though in most decisions involved in making and marketing chess sets, 
it will not be actually relevant, let alone essential). This situational 
character of relevance works both ways: In any particular situation an 



What Computers Can't Do / 170 

indefinite number of facts are possibly relevant and an indefinitely large 
number are irrelevant. Since a computer is not in a situation, however, 
it must treat all facts as possibly relevant at all times. This leaves AI 
workers with a dilemma: they are faced either with storing and accessing 
an infinity of facts, or with having to exclude some possibly relevant facts 
from the computer's range of calculations. 

But even if one could restrict the universe for each particular problem 
to possibly relevant facts and so far this can only be done by the 
programmer, not the program the problem remains to determine what 
information is actually relevant. Even in a nonformal game like playing 
the horses which is much more systematic than everyday open-struc- 
tured problems an unlimited, indefinitely large number of facts remain 
as possibly relevant. In placing a bet we can usually restrict ourselves to 
such facts as the horse's age, jockey, past performance, and competition. 
Perhaps, if restricted to these facts from the racing form, the machine 
could do fairly well, possibly better than an average handicapper; but 
there are always other factors such as whether the horse is allergic to 
goldenrod or whether the jockey has just had a fight with the owner, 
which may in some cases be decisive. Human handicappers are no more 
omniscient than machines, but they are capable of recognizing the rele- 
vance of such facts if they come across them. The artificial intelligence 
approach to this human ability would have to be to give the machine 
knowledge about veterinary medicine, how people behave when they 
fight their employers, and so forth. But then the problem arises of sorting 
through this vast storehouse of data. To which the answer is that all this 
information would be properly coded and tagged in the machine memory 
so that the machine would just have to do a scan for "horse-race betting" 
and get out the relevant material. But not all relevant material would 
have been encoded with a reference to this particular use. As Charles 
Taylor has pointed out in an elaboration of this example: 

The jockey might not be good to bet on today because his mother died yester- 
day. But when we store the information that people often do less than their best 
just after their near relations die, we can't be expected to tag a connection 
with betting on horses. This information can be relevant to an infinite set of con- 
texts. 



Orderly Behavior Without Recourse to Rules / 777 

The machine might select on the basis of the key concepts it was worrying 
about, horses, jockeys, jockey Smith, etc. and pick out all facts about these. But 
this too would give an absurdly wide scatter. Via jockey, man and horse, one 
would find oneself pulling out all facts about centaurs. The only way the machine 
could zero in on the relevant facts would be to take this broad class, or some other 
selected on such a broad swoop basis, and test to see whether each one had causal 
relevance to the outcome of the race, taking it into account if it had, and 
forgetting it if it hadn't. 3 * 

But if the machine were to examine explicitly each possibly relevant 
factor as a determinate bit of information in order to determine whether 
to consider or ignore it, it could never complete the calculations neces- 
sary to predict the outcome of a single race. If, on the other hand, the 
machine systematically excluded possibly relevant factors in order to 
complete its calculations, then it would sometimes be incapable of per- 
forming as well as an intelligent human to whom the same information 
was available. 

Even the appeal to a random element will not help here, since in order 
to take up a sample of excluded possibilities at random so that no 
possibility is in principle excluded, the machine would have to be pro- 
vided with an explicit list of all such other possibly relevant facts or a 
specific set of routines for exploring all classes of possibly relevant facts, 
so that no facts would be in principle inaccessible. This is just what could 
be done in a completely defined system such as chess, where a finite 
number of concepts determines totally and unequivocally the set of all 
possible combinations in the domain; but in the real world the list of such 
possibly relevant facts, or even classes of possibly relevant facts, would 
be indefinitely large ("infinite in a pregnant sense," to use Bar-Hillel's 
phrase). All the everyday problems whether in language translation, 
problem solving, or pattern recognition come back to these two basic 
problems: (1) how to restrict the class of possibly relevant facts while 
preserving generality, and (2) how to choose among possibly relevant 
facts those which are actually relevant. 

Even Minsky implicitly admits that no one knows how to cope with 
the amount of data which must be processed if one simply tries to store 
all facts: 



What Computers Can't Do / 112 

At each moment in the course of thinking about a problem, one is involved with 
a large collection of statements, definitions, associations, and so on, and a net- 
work of goals. One has to deal not only with facts about objects, relations 
between objects, and the like, but also facts about facts, classes of facts, relations 
between such classes, etc. The heuristic programs that, as we shall see, so neatly 
demonstrate principles when applied to small models will not work efficiently 
when applied to large ones. Problems like looping, branching, measuring prog- 
ress, and generally keeping track of what is happening will come to require a 
disproportional part of the computation time. 4 

Whatever it is that enables human beings to zero in on the relevant 
facts without definitively excluding others which might become relevant 
is so hard to describe that it has only recently become a clearly focused 
problem for philosophers. It has to do with the way man is at home in 
his world, has it comfortably wrapped around him, so to speak. Human 
beings are somehow already situated in such a way that what they need 
in order to cope with things is distributed around them where they need 
it, not packed away like a trunk full of objects, or even carefully indexed 
in a filing cabinet. This system of relations which makes it possible to 
discover objects when they are needed is our home or our world. To put 
this less metaphorically it is helpful to return to Charles Taylor's exten- 
sion of the horse-racing example. 

Much of a human being's knowledge of situations and their possibilities is 
know-how, that is, it cannot be exhaustively unpacked into a set of specific 
instructions or factual statements, but is a general capacity to generate appropri- 
ate actions and therefore, if necessary, the "instructions" underlying them. Usu- 
ally we think of this kind of indefinitely unpackable form of knowledge as bound 
up with the know-how which underlies our actions. But the same kind of knowl- 
edge underlies what we suffer, our "passions." Thus just as I have a general grasp 
on what it is to walk around, use my hands, drive a car, conduct a case in court 
(if I'm a lawyer), etc. So I have a general grasp on what it is to be threatened, 
to hear good news, to be jilted by my girl friend, to be made a fool of in public. 
Now the human handicapper has this general grasp of certain common human 
actions and passions. He has the sense of the race as a perilous enterprise which 
needs all the will and effort of jockey (and horse) to win. But included in this 
sense is the capacity to imagine or recognize an indefinite number of ways in 
which this will and effort could miscarry or be countered by fortune. These are 
not stored somewhere as separate facts in the mind or brain, they are not 



Orderly Behavior Without Recourse to Rules / 775 

"unpacked"; they are just generatable from the general grasp of the situation. Of 
course, the general grasp of different men may differ in scope and exactitude. If 
the handicapper has ever ridden horses, then he has a much firmer grasp on the 
activity; he can sense a lot more finely what may go wrong. But even the city-bred 
gangster has some general grasp of what it is to fight and strain hard to win. 
But the artificial intelligence proponent may still want to protest that all this 
just represents an alternative method of "storage." Even if he admits that this 
method is not available to the machine, he might still ask how it solves the 
retrieval problem. How does the handicapper recognize just those odd factors 
which are relevant? The answer is that if we understand our grasp of the world 
as arising out of our dealing with it according to our different capacities, and our 
being touched by it according to our different concerns, then we can see that the 
problem of how a given concern or purpose conies to select the relevant features 
of our surroundings doesn't arise. For being concerned in a certain way or having 
a certain purpose is not something separate from our awareness of our situation; 
it just is being aware of this situation in a certain light, being aware of a situation 
with a certain structure. Thus being anxious for my own life because I have fallen 
among thugs is to sense the menace in that bulge in his pocket, to feel my 
vulnerability to his fist which might at any moment be swung at my face, and 
so on. 5 

The human world, then, is prestructured in terms of human purposes 
and concerns in such a way that what counts as an object or is significant 
about an object already is a function of, or embodies, that concern. This 
cannot be matched by a computer, for a computer can only deal with 
already determinate objects, and in trying to simulate this field of con- 
cern, the programmer can only assign to the already determinate facts 
further determinate facts called values, which only complicates the re- 
trieval problem for the machine. 

In Being and Time Heidegger gives a description of the human world 
in which man is at home, on the model of a constellation of implements 
(Zuege), each referring to each other, to the whole workshop and ulti- 
mately to human purposes and goals. The directional signal on a car 
serves as an example of a "fact" which gets its whole meaning from its 
pragmatic context: 

The directional signal is an item of equipment which is ready-to-hand for the 
driver in his concern with driving, and not for him alone: those who are not 
travelling with him and they in particular also make use of it, either by giving 



What Computers Can't Do / 174 

way on the proper side or by stopping. This sign is ready-to-hand within-the- 
world in the whole equipment-context of vehicles and traffic regulations. It is 
equipment for indicating, and as equipment, it is constituted by reference or 
assignment. 6 

Wittgenstein too makes frequent references to human forms of life and 
concerns and to certain very general "facts of natural history" taken for 
granted in our use of language and in structuring our everyday activities 
facts, incidentally, of a very special kind which would presumably 
elude the programmer trying to program all of human knowledge. As 
Wittgenstein says, "The aspects of things that are most important for us 
are hidden because of their simplicity and familiarity. (One is unable to 
notice something because it is always before one's eyes.)" 7 Facts, more- 
over, which would be so pervasively connected with all other facts that 
even if they could be made explicit, they would be difficult if not impossi- 
ble to classify. The basic insight dominates these discussions that the 
situation is organized from the start in terms of human needs and pro- 
pensities which give the facts meaning, make the facts what they are, so 
that there is never a question of storing and sorting through an enormous 
list of meaningless, isolated data. 

Samuel Todes 8 * has described in detail the field-structure of experi- 
ence which is prior to the facts and implicitly determines their relevance 
and significance. He points out that the world is experienced as fields 
within fields. Bits or aspects of objects are not experienced as isolated 
facts but as nested in a series of contexts. And "in" has many different 
senses, none of them that of mere physical inclusion, which Minsky and 
McCarthy take as primary. Parts of objects are experienced as in objects 
which they comprise, objects are in places which they fill a place is 
situated in a local environment, which itself is in the horizon of possible 
situations in a human world. Data, then, are far from brute; aspects of 
objects are not given as directly in the world but as characterizing objects 
in places in a local environment in space and time in the world. 

We can and do zero in on significant content in the field of experience 
because this field is not neutral to us but is structured in terms of our 
interests and our capacity for getting at what is in it. Any object which 
we experience must appear in this field and therefore must appear in 



Orderly Behavior Without Recourse to Rules / 775 

terms of our dominant interest at that moment, and as attainable by some 
variant of the activity which generated the field. Since we create the field 
in terms of our interests, only possibly relevant facts can appear. 

Relevance is thus already built in. In the horse race case, racing fits 
into a nested context of activities, games, sports, contests. To see an 
activity as a horse race is to organize it in terms of the intention to win. 
To return to Taylor's account: 

The handicapper is concerned to pick a winner. As a human being he has a sense 
of what is involved in the enterprise of winning, and his being concerned means 
that he is aware of a horse, jockey, etc., in a way in which dangers are salient. 
Hence he notices when he reads in the obituary columns that Smith's mother 
died yesterday (Smith being the jockey, and one he knows to be very susceptible), 
and for once he bets against the form. The machine would pick out Smith's 
mother's death, as a fact about Smith, along with all the others, such as that 
Smith's second cousin has been elected dogcatcher in some other city, etc., but 
will then have to do a check on the probable consequences of these different facts 
before it decides to take them into account or not in placing the bet. 9 

Thus our present concerns and past know-how always already deter- 
mines what will be ignored, what will remain on the outer horizon of 
experience as possibly relevant, and what will be immediately taken into 
account as essential. 

Wittgenstein constantly suggests that the analysis of a situation into 
facts and rules (which is where the traditional philosopher and the 
computer expert think they must begin) is itself only meaningful in some 
context and for some purpose. Thus again the elements already reflect 
the goals and purposes for which they were carved out. When we try to 
find the ultimate context-free, purpose-free elements, as we must if we 
are going to find the ultimate bits to feed a machine bits that will be 
relevant to all possible tasks because chosen for none we are in effect 
trying to free the facts in our experience of just that pragmatic organiza- 
tion which makes it possible to use them flexibly in coping with everyday 
problems. 

Not that a computer model is ever really purpose-free; even a model 
in terms of information storage must somehow reflect the context, but 
such an analysis of context in terms of facts and rules is rigid and 



What Computers Can't Do / 176 

restricting. To see this, let us grant that all the properties of objects 
(whatever that might mean) could be made explicit in a decision tree so 
that each node recorded whether the object has a certain situation- 
independent predicate or its converse. This sort of classification structure 
has been programmed by Edward Feigenbaum in his EPAM model. 10 * 
Such a discrimination net might, in principle, represent an exhaustive, 
explicit, apparently situation-free characterization of an object, or even 
of a situation, insofar as it was considered as an object. It thus seems to 
provide efficient information storage, while avoiding the field/object 
distinction. But something crucial is omitted in the description of such 
an information structure: the organization of the structure itself, which 
plays a crucial role in the informative storage. The information in the tree 
is differently stored and differently accessible depending on the order in 
which the discriminations are made. As William Wynn notes in a discus- 
sion of EPAM: 

EPAM's Classification process is ... too history-dependent and unadaptable, for 
the discrimination net can be grown only from the bottom down and cannot be 
reorganized from the top. Tests inserted in the net which later prove to be of little 
discriminatory power over a given stimulus set cannot be removed, nor can new 
tests be inserted in the upper portion of the net. Thus, once it is formed, EPAM's 
discrimination net is difficult to reorganize in the interest of greater retrieval 
efficiency. Any procedure that reorganizes the tests in the structure seriously 
impairs retrieval of many items held in the memory. 11 

So the order of discriminations is crucial. But in the physical world 
all predicates have the same priority. Only the programmer's sense of the 
situation determines the order in the decision tree. Through the pro- 
grammer's judgment the distinction between the field and the objects in 
the field is introduced into the computerized model. The pragmatic 
context used by the programmer can indeed itself be characterized in a 
decision tree, but only in some order of discriminations which reflects a 
broader context. At each level information concerning this broader con- 
text is indeed embodied in the general structure of the tree, but at no 
particular node. At each level the situation is reflected in the pragmatic 
intuitions of the programmer governing the order of decisions; but this 



Orderly Behavior Without Recourse to Rules / 777 

fixes the facts in one order based on a particular purpose, and inevitably 
introduces the lack of flexibility noted by Wynn. 

If, on the other hand, in the name of flexibility all pragmatic ordering 
could be eliminated so that an unstructured list of purified facts could 
be assimilated by machine facts about the sizes and shapes of objects 
in the physical world and even about their possible uses, as isolable 
functions then all these facts would have to be explicitly included or 
excluded in each calculation, and the computer would be overwhelmed 
by their infinity. 

This is not to deny that human beings sometimes take up isolated data 
and try to discover their significance by trying to fit them into a previ- 
ously accumulated store of information. Sherlock Holmes and all detec- 
tives do this as a profession; everyone does it when he is in a very 
unfamiliar situation. But even in these cases there must be some more 
general context in which we are at home. A Martian might have to 
proceed in a very unfamiliar context if he were on earth, but if he shared 
no human purposes his task of sorting out the relevant from the irrele- 
vant, essential from the inessential, would be as hopeless as that of the 
computer. 

We all know also what it is to store and use data according to rules 
in some restricted context. We do this, for example, when we play a game 
such as bridge, although even here a good bridge player stores data in 
terms of purpose and strategies and takes liberties with the heuristic 
rules. We also sometimes play out alternatives in our imagination to 
predict what will happen in the real game before us. But it is just because 
we know what it is to have to orient ourselves in a world in which we 
are not at home; or to follow rulelike operations like the heuristics for 
bidding in bridge; and how to model in our imagination events which 
have not yet taken place, that we know that we are not aware of doing 
this most of the time. The claim that we are nonetheless carrying on such 
operations unconsciously is either an empirical claim, for which there is 
no evidence, or an a priori claim based on the very assumption we are 
here calling into question. 

When we are at home in the world, the meaningful objects embedded 



What Computers Can't Do / 178 

in their context of references among which we live are not a model of 
the world stored in our mind or brain; they are the world itself. This may 
seem plausible for the public world of general purposes, traffic regula- 
tions, and so forth. But what about my experience, one may ask; my 
private set of facts, surely that is in my mind? This seems plausible only 
because one is still confusing this human world with some sort of physi- 
cal universe. My personal plans and my memories are inscribed in the 
things around me just as are the public goals of men in general. My 
memories are stored in the familiar look of a chair or the threatening air 
of a street corner where I was once hurt. My plans and fears are already 
built into my experience of some objects as attractive and others as to 
be avoided. The "data" concerning social tasks and purposes which are 
built into the objects and spaces around me are overlaid with these 
personal "data" which are no less a part of my world. After all, personal 
threats and attractions are no more subjective than general human pur- 
poses. 

Now we can see why, even if the nervous system must be understood 
as a physical object a sort of analogue computer whose energy ex- 
change with the world must in principle be expressible as an input/out- 
put function, it begs the question and leads to confusion to suppose that 
on the information-processing level the human perceiver can be under- 
stood as an analogue computer having a precise I/O function reproduci- 
ble on a digital machine. The whole I/O model makes no sense here. 
There is no reason to suppose that the human world can be analyzed into 
independent elements, and even if it could, one would not know whether 
to consider these elements the input or the output of the human mind. 

If this idea is hard to accept, it is because this phenomenological 
account stands in opposition to our Cartesian tradition which thinks of 
the physical world as impinging on our mind which then organizes it 
according to its previous experience and innate ideas or rules. But even 
Descartes is not confused in the way contemporary psychologists and 
artificial intelligence researchers seem to be. He contends that the world 
which impinges on us is a world of pure physical motions, while the world 
"in the mind" is the world of objects, instruments, and so forth. Only 
the relation between these two worlds is unclear. Artificial intelligence 



Orderly Behavior Without Recourse to Rules / 779 

theorists such as Minsky, however, have a cruder picture in which the 
world of implements does not even appear. As they see it, details of the 
everyday world snapshots, as it were, of tables, chairs, etc. are re- 
ceived by the mind. These fragments are then reassembled in terms of 
a model built of other facts the mind has stored up. The outer world, a 
mass of isolated facts, is interpreted in terms of the inner storehouse of 
other isolated, but well catalogued, facts which somehow was built up 
from earlier experiences of this fragmented world and the result is a 
further elaboration of this inner model. Nowhere do we find the familiar 
world of implements organized in terms of purposes. 

Minsky has elaborated this computer-Cartesianism into an attempt at 
philosophy. He begins by giving a plausible description of what is in fact 
the role of imagination: 

If a creature can answer a question about a hypothetical experiment without 
actually performing it, then it has demonstrated some knowledge about the 
world. For, his [sic] answer to the question must be an encoded description of 
the behavior (inside the creature) of some submachine or "model" responding 
to an encoded description of the world situation described by the question. 12 

Minsky then, without explanation or justification, generalizes this plausi- 
ble description of the function of imagination to all perception and 
knowledge: 

Questions about things in the world are answered by making statements about 
the behavior of corresponding structures in one's model of the world. 13 

He is thus led to introduce a formalized copy of the external world; as 
if besides the objects which solicit our action, we need an encyclopedia 
in which we can look up where we are and what we are doing: 

A man's model of the world has a distinctly bipartite structure: One part is 
concerned with matters of mechanical, geometrical, physical character, while the 
other is associated with things like goals, meanings, social matters, and the like. 14 

If all knowledge requires a model we, of course, need a model of 
ourselves: 

When a man is asked a general question about his own nature, he will try to give 
a general description of his model of himself. 15 



What Computers Can't Do / 180 

And, of course, for this self-description to be complete we will need 
a description of our model of our model of ourselves, and so forth. 
Minsky thinks of this self-referential regress as the source of philosoph- 
ical confusions concerning mind, body, free will, and so on. He does not 
realize that his insistence on models has introduced the regress and that 
this difficulty is proof of the philosophical incoherence of his assumption 
that nothing is ever known directly but only in terms of models. 

In general the more one thinks about this picture the harder it is to 
understand. There seem to be two worlds, the outer data- and the inner 
data-structure, neither of which is ever experienced and neither of which 
is the physical universe or the world of implements we normally do 
experience. There seems to be no place for the physical universe or for 
our world of interrelated objects, but only for a library describing the 
universe and human world which, according to the theory, cannot 
exist. 

To dismiss this theory as incoherent is not to deny that physical energy 
bombards our physical organism and that the result is our experience of 
the world. It is simply to assert that the physical processing of the 
physical energy is not a psychological process, and does not take place 
in terms of sorting and storing human-sized facts about tables and chairs. 
Rather, the human world is the result of this energy processing and the 
human world does not need another mechanical repetition of the same 
process in order to be perceived and understood. 

This point is so simple and yet so hard to grasp for those brought up 
in the Cartesian tradition that it may be necessary to go over the ground 
once again, this time returning to a specific case of this confusion. As we 
have seen, Neisser begins his book Cognitive Psychology with an exposi- 
tion of what he calls "the central problem of cognition." 

There is certainly a real world of trees and people and cars and even books. 
. . . However, we have no direct, immediate access to the world, nor to any of 
its properties. 16 

Here, as we have noted in Chapter 4, the damage is already done. There 
is indeed a world to which we have no immediate access. We do not 
directly perceive the world of atoms and electromagnetic waves (if it even 



Orderly Behavior Without Recourse to Rules / 181 

makes sense to speak of perceiving them) but the world of cars and 
books is just the world we do directly experience. In Chapter 4 we saw 
that at this point, Neisser has recourse to an unjustified theory that we 
perceive "snapshots" or sense data. His further account only compounds 
the confusion: 

Physically, this page is an array of small mounds of ink, lying in certain positions 
on the more highly reflective surface of the paper. 17 

But physically, what is there are atoms in motion, not paper and small 
mounds of ink. Paper and small mounds of ink are elements in the 
human world. Neisser, however, is trying to look at them in a special 
way, as if he were a savage, a Martian, or a computer, who didn't know 
what they were for. There is no reason to suppose that these strangely 
isolated objects are what men directly perceive (although one may per- 
haps approximate this experience in the very special detached attitude 
which comes over a cognitive psychologist sitting down to write a book). 
What we normally perceive is a printed page. 

Again Neisser's middle-world, which is neither the world of physics 
nor the human world, turns out to be an artifact. No man has ever seen 
such an eerie world; and no physicist has any place for it in his system. 
Once we postulate it, however, it follows inevitably that the human world 
will somehow have to be reconstructed out of these fragments. 

One-sided in their perspective, shifting radically several times each second, 
unique and novel at every moment, the proximal stimuli bear little resemblance 
to either the real object that gave rise to them or to the object of experience that 
the perceiver will construct as a result. 18 

But this whole construction process is superfluous. It is described in 
terms which make sense only if we think of man as a computer receiving 
isolated facts from a world in which it has no purposes; programmed to 
use them, plus a lot of other meaningless data it has accumulated or been 
given, to make some sort of sense (whatever that might mean) out of 
what is going on around it. 

There is no reason to suppose that a normal human being has this 
problem, although some aphasics do. A normal person experiences the 



What Computers Can't Do / 182 

objects of the world as already interrelated and full of meaning. There 
is no justification for the assumption that we first experience isolated 
facts, or snapshots of facts, or momentary views of snapshots of isolated 
facts, and then give them significance. The analytical superfluousness of 
such a process is what contemporary philosophers such as Heidegger and 
Wittgenstein are trying to point out. To put this in terms of Neisser's 
discussion as nearly as sense will allow, we would have to say: "The 
human world is the mind's model of the physical world." But then there 
is no point in saying it is "in the mind," and no point in inventing a third 
world between the physical and the human world which is an arbi- 
trarily impoverished version of the world in which we live, out of which 
this world has to be built up again. 

Oettinger, alone among computer experts, has seen that in the world 
of perception and language, where the linguist and artificial intelligence 
worker begins his analysis, a global meaning is always already present. 

What I want to suggest is not necessarily a novel suggestion; but it does seem 
to have been lost from sight, perhaps deservedly so, because, as I have pointed 
out, it doesn't tell one what to do next. What I suggest is that it almost seems 
as if the perception of meaning were primary and everything else a consequence 
of understanding meaning. 19 

But Oettinger does not seem to see that if one simply looks for some new 
sort of process, by which this global meaning is "produced," thereby 
reversing the current misunderstanding, one is bound to find what seems 
a mystery or a dead end. 

When we try to turn this around and say, "Well now, here is this stream of sound 
coming at you or its equivalent on a printed page, and what is it that happens 
to your listening to me or in reading a printed page that enables you to react to 
the meaning of what I say?" we seem to hit a dead end at this point. 20 

What Oettinger too fails to understand is that there is either a stream 
of sounds or there is meaningful discourse. The meaning is not produced 
from meaningless elements, be they marks or sounds. The stream of 
sounds is a problem for physics and neurophysiology, while on the level 
of meaningful discourse, the necessary energy processing has already 
taken place, and the result is a meaningful world for which no new 



Orderly Behavior Without Recourse to Rules / 183 

theory of production is required nor can be consistently conceived. 

To avoid inventing problems and mysteries we must leave the physical 
world to the physicists and neurophysiologists, and return to our descrip- 
tion of the human world which we immediately perceive. The problem 
facing contemporary philosophers is to describe the context or situation 
in which human beings live, without importing prejudices from the 
history of philosophy or the current fascination with computer models. 
This brings us back to the problem of regularity and rules. 

Our context-guided activity in terms of which we constantly modify 
the relevance and significance of particular objects and facts is quite 
regular, but the regularity need not and cannot be completely rule gov- 
erned. As in the case of ambiguity tolerance, our activity is simply as rule 
governed as is necessary for the task at hand the task itself, of course, 
being no more precise than the rules. 

Wittgenstein, like Heidegger, sees the regulation of traffic as paradig- 
matic: 

The regulation of traffic in the streets permits and forbids certain actions on the 
part of drivers and pedestrians; but it does not attempt to guide the totality of 
their movements by prescription. And it would be senseless to talk of an 'ideal* 
ordering of traffic which would do that; in the first place we should have no idea 
what to imagine as this ideal. If someone wants to make traffic regulations stricter 
on some point or other, that does not mean that he wants to approximate to such 
an ideal. 21 

This contextual regularity, never completely rule governed, but always 
as orderly as necessary, is so pervasive that it is easily overlooked. Once, 
however, it has been focused on as the background of problem solving, 
language use, and other intelligent behavior, it no longer seems necessary 
to suppose that all ordered behavior is rule governed. The rule-model 
only seems inevitable if one abstracts himself from the human situation 
as philosophers have been trying to do for two thousand years, and as 
computer experts must, given the context-free character of information 
processing in digital machines. 



The Situation as a Function of Human Needs 



We are at home in the world and can find our way about in it because 
it is our world produced by us as the context of our pragmatic activity. 
So far we have been describing this world or situation and how it enables 
us to zero in on significant objects in it. We have also suggested that this 
field of experience is structured in terms of our tasks. These are linked 
to goals, and these in turn correspond to the social and individual needs 
of those whose activity has produced the world. 

What does this tell us about the possibility of AI? If the data which 
are to be stored and accessed are normally organized in terms of specific 
goals, then it would seem that the large data base problem confronting 
AI could be solved if one just constructed a list of objectives and their 
priorities what computer workers dealing with decision-making pro- 
grams call a utility function and programmed it into the computer 
along with the facts. 

We have seen, however, that explicit objectives do not work, even for 
organizing simple problem-solving programs. The difficulties of simple 
means-ends analysis suggest that in order for the computer to solve even 
well-structured problems, it is not sufficient for the machine to have an 
objective and to measure its progress toward this preset end. Planning 
requires finding the essential operations, so "pragmatic considerations," 
for example, the relative importance of logical operations had to be 



The Situation as a Function of Human Needs / 185 

surreptitiously supplied by the programmers themselves before the logic 
program could begin. We must now try to describe in more detail how 
this pragmatic structuring differs from means-ends analysis, ultimately 
asking, of course, whether this human capacity for purposive organiza- 
tion is in principle programmable on digital machines. 

The difference between human goals and machine ends or objectives 
has been noted by one scientist who has himself been working on pattern 
recognition. Satosi Watanabe describes this difference as follows: 

For man, an evaluation is made according to a system of values which is non- 
specific and quasi-emotive, while an evaluation for a robot could only be made 
according to a table or a specific criterion. . . . This difference is subtle but 
profound. [One might say] that a man has values while a machine has objectives. 
Certainly men too have objectives, but these are derived from a system of values 
and are not the final arbiter of his actions, as they would be for a robot. 
... As soon as the objective is set the machine can pursue it just as the man can. 
Likewise human utilitarian behavior can be easily simulated by a machine if the 
quantitative utility and the probability of each alternative event is fixed and given 
to the machine. But a machine can never get at the source from which this utility 
is derived. 1 

Watanabe claims that these values are essential to intelligent behavior. 
For one thing, as Watanabe points out, "there are infinitely many possi- 
ble hypotheses that are supported by experience. Limitation of these 
hypotheses to a smaller subset is often done by a vaguely conceived 
criterion, such as the principle of simplicity, or the principle of ele- 
gance." 2 More specifically, Watanabe argues that it can be demonstrated 
that any two objects have the same number of predicates in common. If 
this does not seem to us to be the case, it is because we consider certain 
predicates more important than others. This decision as to what is impor- 
tant depends on our system of values. 3 

But why on our system of values and not on a list of objectives? How 
does what Watanabe calls a system of values differ from having a utility 
function? So far the only difference seems to be that values are vaguer. 
But throughout Watanabe's analysis there is no argument showing why 
these values are not just vague objectives which could be represented by 
a region on a quantitative scale. To understand this important difference, 



What Computers Can't Do / 186 

which Watanabe has noted, but not explained, one must first abandon 
his way of posing the problem. To speak of values already gives away 
the game. For values are a product of the same philosophical tradition 
which has laid down the conceptual basis of artificial intelligence. Al- 
though talk of values is rather new in philosophy, it represents a final 
stage of objectification in which the pragmatic considerations which 
pervade experience and determine what counts as an object are conceived 
of as just further characteristics of independent objects, such as their 
hardness or color. A value is one more property that can be added to or 
subtracted from an object. Once he has adopted this terminology and the 
philosophical position it embodies, Watanabe is unable to explain how 
values differ from somewhat vague properties, and thus cannot explain 
why he feels they cannot be programmed. To understand the fundamen- 
tal difficulty Watanabe is trying to get at, we must be able to distinguish 
between objects, and the field or situation which makes our experience 
of objects possible. For what Watanabe misleadingly calls values belongs 
to the structure of the field of experience, not the objects in it. 

We have seen that experience itself is organized in terms of our tasks. 
Like the pattern of a chess game, the world is a field in which there are 
areas of attraction and repulsion, paths of accessibility, regions of activity 
and of repose. In our own perceptual world we are all master players. 
Objects are already located and recognized in a general way in terms of 
the characteristics of the field they are in before we zero in on them and 
concern ourselves with their details. It is only because our interests are 
not objects in our experience that they can play this fundamental role 
of organizing our experience into meaningful patterns or regions. 

Heidegger has described the way human concerns order experiences 
into places and regions: 

Equipment has its place or else it 'lies around': this must be distinguished in 
principle from just occurring at random in some spacial position. . . . The kind 
of place which is constituted by direction and remoteness (and closeness is only 
a mode of the latter) is already oriented towards a region and oriented within 

it Thus anything constantly ready-to-hand of which circumspective Being-in- 

the- World takes account beforehand has its place. The * where' of its readiness-to- 
hand is put to account as a matter for concern. . . .* 



The Situation as a Function of Human Needs / 187 

Heidegger is also the first to have called attention to the way philoso- 
phy has from its inception been dedicated to trying to turn the concerns 
in terms of which we live into objects which we could contemplate and 
control. Socrates was dedicated to trying to make his and other people's 
commitments explicit so that they could be compared, evaluated, and 
justified. But it is a fundamental and strange characteristic of our lives 
that insofar as we turn our most personal concerns into objects, which 
we can study and choose, they no longer have a grip on us. They no 
longer organize a field of significant possibilities in terms of which we act 
but become just one more possibility we can choose or reject. Philoso- 
phers thus finally arrived at the nihilism of Nietzsche and Sartre in which 
personal concerns are thought of as a table of values which are arbitrarily 
chosen and can be equally arbitrarily abandoned or transvaluated. Ac- 
cording to Nietzsche, "The great man is necessarily a skeptic. . . . 
Freedom from any kind of conviction is part of the strength of his 
will." 5 * 

But what is missing in this picture besides a sense of being gripped by 
one's commitment? What difference does it make when one is trying to 
produce intelligent behavior that one's evaluations are based on a util- 
ity function instead of some ultimate concern? One difference, which 
Watanabe notes without being able to explain, is that a table of values 
must be specific, whereas human concerns only need to be made as 
specific as the situation demands. This flexibility is closely connected 
with the human ability to recognize the generic in terms of purposes, and 
to extend the use of language in a regular but nonrulelike way. Moreover, 
man's ultimate concern is not just to achieve some goal which is the end 
of a series; rather, interest in the goal is present at each moment structur- 
ing the whole of experience and guiding our activity as we constantly 
select what is relevant in terms of its significance to the situation at hand. 6 
A machine table of objectives, on the other hand, has only an arbitrary 
relation to the alternatives before the machine, so that it must be explic- 
itly appealed to at predetermined intervals to evaluate the machine's 
progress and direct its next choice. 

Herbert Simon and Walter Reitman have seen that emotion and moti- 
vation play some role in intelligent behavior, but their way of simulating 



What Computers Can't Do / 188 

this role is to write programs where "emotions" can interrupt the work 
on one problem to introduce extraneous factors or work on some other 
problem. 7 They do not seem to see that emotions and concerns accom- 
pany and guide our cognitive behavior. This is again a case of not being 
able to see what one would not know how to program. 

Heidegger tries to account for the pervasive concern organizing hu- 
man experience in terms of a basic human need to understand one's 
being. But this analysis remains very abstract. It accounts for significance 
in general but not for any specific goal or specific significance. Thus 
Heidegger in effect assimilates all human activity to creative problem 
solving or artistic creation where we do not fully know what our goal 
was until we have achieved it. For Heidegger there can be no list of 
specifications which the solution must fulfill. Still, our needs are determi- 
nate enough to give things specific meaning for us, and many of our goals 
are quite explicit. To understand this we require a more concrete 
phenomenological analysis of human needs. 

The philosophical and psychological tradition (with the exception of 
the pragmatists), however, has tried to ignore the role of these needs in 
intelligent behavior, and the computer model has reinforced this ten- 
dency. Thus N. S. Sutherland, Professor of Experimental Psychology at 
the University of Sussex, in an article "Machines and Men," writes: 

Survival and self maintenance are achieved by genetically building into the 
human brain a series of drives or goals. Some of the obvious ones are hunger, 
thirst, the sexual drive and avoidance of pain. All of these drives are parochial 
in the sense that one could imagine complex information processing systems 
exhibiting intelligent behavior but totally lacking them. 8 

We have seen, however, that our concrete bodily needs directly or 
indirectly give us our sense of the task at hand, in terms of which our 
experience is structured as significant or insignificant. These needs have 
a very special structure, which, while more specific than Heidegger's 
account, does resemble artistic creation. When we experience a need we 
do not at first know what it is we need. We must search to discover what 
allays our restlessness or discomfort. This is not found by comparing 
various objects and activities with some objective, determinate criterion, 



The Situation as a Function of Human Needs / 189 

but through what Todes calls our sense of gratification. This gratification 
is experienced as the discovery of what we needed all along, but it is a 
retroactive understanding and covers up the fact that we were unable to 
make our need determinate without first receiving that gratification. The 
original fulfillment of any need is, therefore, what Todes calls a creative 
discovery. 9 * 

Thus human beings do not begin with a genetic table of needs or values 
which they reveal to themselves as they go along. Nor, when they are 
authentic, do they arbitrarily adopt values which are imposed by their 
environment. Rather, in discovering what they need they make more 
specific a general need which was there all along but was not determinate. 

This is most obvious when dealing with less instinctual psychological 
needs. When a man falls in love he loves a particular woman, but it is 
not that particular woman he needed before he fell in love. However, 
after he is in love, that is after he has found that this particular relation- 
ship is gratifying, the need becomes specific as the need for that particu- 
lar woman, and the man has made a creative discovery about himself. 
He has become the sort of person that needs that specific relationship and 
must view himself as having lacked and needed this relationship all 
along. In such a creative discovery the world reveals a new order of 
significance which is neither simply discovered nor arbitrarily chosen. 

Soren Kierkegaard has a great deal to say about the way one's person- 
ality or self is redefined in such an experience, and how everything in a 
person's world gets a new level of meaning. Since such a change, by 
modifying a person's concerns, changes the whole field of interest in 
terms of which everything gets its significance, Kierkegaard speaks of 
these fundamental changes as changes in our sphere of existence. And 
because such a change cannot be predicted on the basis of our previous 
concerns, yet once it has taken place is so pervasive that we cannot 
imagine how it could have been otherwise, Kierkegaard speaks of a 
change of sphere of existence as a leap. 10 

This same sort of change of world can take place on a conceptual level. 
Then it is called a conceptual revolution. Thomas Kuhn in his book The 
Structure of Scientific Revolutions has studied this sort of transforma- 



What Computers Can't Do / 790 

tion. As he puts it: "Insofar as their only recourse to that world is 
through what they see and do, we may want to say that after a revolution 
scientists are responding to a different world." 11 

The conceptual framework determines what counts as a fact. Thus 
during a revolution there are no facts to which scientists can appeal to 
decide which view is correct. 'The data themselves [have] changed. This 
is the [sense] in which we may want to say that after a revolution 
scientists work in a different world." 12 The idea that knowledge consists 
of a large store of neutral data, taken for granted by Minsky, is inade- 
quate to account for these moments of profound change. According to 
Kuhn, "there can be no scientifically or empirically neutral system of 
language or concepts." 13 

What occurs during a scientific revolution is not fully reducible to a reinterpreta- 
tion of individual and stable data. In the first place the data are not unequivocally 
stable. A pendulum is not a falling stone, nor is oxygen dephlogisticated air. 14 

This leads Kuhn to a rejection of the whole philosophical tradition 
which has culminated in the notion of reason as based on the storage and 
processing of "data." On the basis of his research Kuhn sees both the 
inadequacy of this tradition and why it nonetheless continues to seem 
self-evident. 

Are theories simply man-made interpretations of given data? The epistemologi- 
cal viewpoint that has most often guided Western philosophy for three centuries 
dictates an immediate and unequivocal, Yes! In the absence of a developed 
alternative, I find it impossible to relinquish entirely that viewpoint. Yet it no 
longer functions eifectively, and the attempts to make it do so through the 
introduction of a neutral language of observations now seem to me hopeless. 15 

In suggesting an alternative view, or more exactly, in analyzing the 
way science actually proceeds so as to provide the elements of an alterna- 
tive view, Kuhn focuses on the importance of a paradigm, that is, a 
specific accepted example of scientific practice, in guiding research. Here, 
as in the case of family resemblance studied earlier, objects are under- 
stood not in terms of general rules but rather in terms of their relation 
to a specific concrete case whose traits or implications cannot be com- 
pletely formalized. 



The Situation as a Function of Human Needs / 191 

[Scientists can] agree in their identification of a paradigm without agreeing on, 
or even attempting to produce, a full interpretation or rationalization of it. Lack 
of a standard interpretation or of an agreed reduction to rules will not prevent 
a paradigm from guiding research. . . . Indeed, the existence of a paradigm need 
not even imply that any full set of rules exist. 16 

It is just this open-ended richness of paradigms which makes them 
important: 

Paradigms may be prior to, more binding, and more complete than any set of 
rules for research that could be unequivocally abstracted from them. 17 

Without such paradigms scientists confront the world with the same 
bewilderment which we have suggested would necessarily confront an 
AI researcher trying to formalize the human form of life: 

In the absence of a paradigm ... all of the facts that could possibly pertain to 
the development of a given science are likely to seem equally relevant. 18 

Indeed, without a paradigm it is not even clear what would count as 
a fact, since facts are produced in terms of a particular paradigm for 
interpreting experience. Thus finding a new paradigm is like a Kierke- 
gaardian leap: 

Just because it is a transition between incommensurables, the transition between 
competing paradigms cannot be made a step at a time, forced by logic and neutral 
experience. Like the gestalt switch, it must occur all at once (though not neces- 
sarily in an instant) or not at all. 19 

Here it becomes clear that the idea of problem solving as simply 
storing and sorting through data with a specific end in view can never 
do justice to these fundamental conceptual changes, yet these changes 
determine the conceptual space in which problems can first be posed and 
in terms of which data get their pervasive character of relevance and 
significance, so that problems can be solved. The reigning conceptual 
framework implicitly guides r esearch just as the perceptual field guides 
our perception of objects. 

Finally, even more fundamental than these conceptual revolutions 
studied by Kuhn are cultural revolutions; for example, the beginning of 
Greek philosophy, as we have seen, set up a view of the nature of man 



What Computers Can't Do / 192 

and rationality on which all subsequent conceptual revolutions have 
rung changes. Equally radically, with the beginning of Christianity a new 
kind of love became possible which was not possible in Greece; heroism 
became suspect as a sign of pride, and goodness came to consist in the 
sacrifices of saints. These cultural revolutions show us, as Pascal first 
pointed out, that there is no sharp boundary between nature and culture 
even instinctual needs can be modified and overridden in terms of 
paradigms thus there is no fixed nature of man. 

Man's nature is indeed so malleable that it may be on the point of 
changing again. If the computer paradigm becomes so strong that people 
begin to think of themselves as digital devices on the model of work in 
artificial intelligence, then, since for the reasons we have been rehearsing, 
machines cannot be like human beings, human beings may become 
progressively like machines. During the past two thousand years the 
importance of objectivity; the belief that actions are governed by fixed 
values; the notion that skills can be formalized; and in general that one 
can have a theory of practical activity, have gradually exerted their 
influence in psychology and in social science. People have begun to think 
of themselves as objects able to fit into the inflexible calculations of 
disembodied machines: machines for which the human form-of-life must 
be analyzed as a meaningless list of facts, rather than the flexible prera- 
tional basis of rationality. Our risk is not the advent of super-intelligent 
computers, but of subintelligent human beings. 



Conclusion 



This alternative conception of man and his ability to behave intelligently 
is really an analysis of the way man's skillful bodily activity as he works 
to satisfy his needs generates the human world. And it is this world 
which sets up the conditions under which specific facts become accessible 
to man in indefinite and open-ended ways, because these facts are origi- 
nally organized in terms of these needs. This enables us to see the 
fundamental difference between human and machine intelligence. Artifi- 
cial intelligence must begin at the level of objectivity and rationality 
where the facts have already been produced. It abstracts these facts 1 * 
from the situation in which they are organized and attempts to use the 
results to simulate intelligent behavior. But these facts taken out of 
context are an unwieldy mass of neutral data with which artificial intelli- 
gence workers have thus far been unable to cope. All programs so far 
"bog down inexorably as the information files grow." 2 

No other data-processing techniques exist at present besides the ac- 
cumulation of facts, and once the traditional philosophical assumptions 
underlying work in artificial intelligence have been called into question 
there is no reason to suppose that digital data storage and retrieval 
techniques will ever be powerful enough to cope with the amount of data 
generated when we try to make explicit our knowledge of the world. 
Since the data about the world may well be infinite and the formalization 



/ 793 



What Computers Can't Do / 194 

of our form-of-life may well be impossible, it would be more reasonable 
to suppose that digital storage techniques can never be up to the task. 

"Moreover, if this phenomenological description of human intelligence 
is correct, there are in principle reasons why artificial intelligence can 
never be completely realized. Besides the technological problem posed 
by storing a great number of bits of neutral data, there are in the last 
analysis no fixed facts, be they a million or ten million, as Minsky would 
like to believe. Since human beings produce facts, the facts themselves 
are changed by conceptual revolutions. 

Finally, if the philosopher or artificial intelligence researcher proposes 
to meet this objection by formalizing the human needs which generate 
this changing context, he is faced with the source of this same difficulty. 
Indeterminate needs and goals and the experience of gratification which 
guides their determination cannot be simulated on a digital machine 
whose only mode of existence is a series of determinate states. Yet, it is 
just because these needs are never completely determined for the individ- 
ual and for mankind as a whole that they are capable of being made more 
determinate, and human nature can be retroactively changed by individ- 
ual and cultural revolutions. 



CONCLUSION: THE SCOPE AND 



LIMITS OF ARTIFICIAL REASON 



1O 



The Limits of Artificial Intelligence 



We are now in a position to draw together the various strands of our 
philosophical argument concerning the limits of artificial intelligence. 
The division of the field of artificial intelligence into two subfields, Cogni- 
tive Simulation (CS) and Artificial Intelligence (AI), has led to the 
treatment of two separate but interrelated questions: (1) Does a human 
being in "processing information" actually follow formal rules like a 
digital computer?, and (2) Can human behavior, no matter how gener- 
ated, be described in a formalism which can be manipulated by a digital 
machine? 

In discussing each of these questions we found, first, that the des- 
criptive or phenomenological evidence, considered apart from tradi- 
tional philosophical prejudices, suggests that nonprogrammable human 
capacities are involved in all forms of intelligent behavior. Moreover, we 
saw that no contrary empirical evidence stands up to methodological 
scrutiny. Thus, insofar as the question whether artificial intelligence is 
possible is an empirical question, the answer seems to be that further 
significant progress in Cognitive Simulation or in Artificial Intelligence 
is extremely unlikely. 

If in the face of these difficulties workers in artificial intelligence still 
wish to justify their optimism, the burden of proof is henceforth on them. 
They must show that despite the empirical difficulties artificial intelli- 



/ 797 



What Computers Can't Do / 198 

gence must be possible. But the a priori case for artificial intelligence is 
even weaker here than the empirical one. The very arguments which are 
supposed to show that formalization must be possible turn out to be 
either incoherent or self-contradictory and show, on the contrary, that 
barring certain highly unlikely empirical assumptions which have been 
ruled out by common agreement, formalization is impossible. The a 
priori arguments for formalization thus turn into conditional in principle 
arguments against the possibility of CS and AI. 

Let us review these arguments in more detail. In discussing CS we 
found that in playing games such as chess, in solving complex problems, 
in recognizing similarities and family resemblances, and in using lan- 
guage metaphorically and in ways we feel to be odd or ungrammatical, 
human beings do not seem to themselves or to observers to be following 
strict rules. On the contrary, they seem to be using global perceptual 
organization, making pragmatic distinctions between essential and ines- 
sential operations, appealing to paradigm cases, and using a shared sense 
of the situation to get their meanings across. 

Of course, all this orderly but apparently nonrulelike activity might 
nonetheless be the result of unconsciously followed rules. But when one 
tries to understand this as a philosophical proposal that all behavior 
must be understood as following from a set of instructions, one finds a 
regress of rules for applying rules. This regress cannot be terminated by 
an appeal to ordinary facts for, according to the original claim, the facts 
must themselves always be recognized and interpreted by rule. 

One way to avoid this regress would be to claim that the ultimate data 
are inputs of physical energy and that such inputs can always be digital- 
ized and processed according to rule. This seems to be Fodor's view. The 
claim that these inputs are processed in a sequence of operations like a 
digital program is not unintelligible, but would, as Fodor admits, de- 
mand an incredibly complex formalism which no one has been able to 
discover or invent. In the absence of any empirical or a priori argument 
that such a formalism for processing physical inputs does or must exist, 
and given the empirical evidence that the brain functions like an ana- 
logue computer, there is no reason to suppose and every reason to doubt 



Conclusion / 799 

that the processing of physical inputs in the human brain takes the form 
of a digital computer program. 

The only other way to avoid the regress of rules is to modify the thesis 
and claim that on the lowest level rules are automatically applied without 
instructions. But this leads to trouble in two ways: (1) Once the a priori 
thesis that all behavior must follow instructions is thus weakened, we 
might as well claim that skilled behavior need not be based on uncon- 
sciously followed instructions at any level, so the argument that in spite 
of the phenomenological evidence subjects must be following rules must 
be abandoned. 

(2) If one nonetheless insists that there must be an ultimate level of 
uninterpreted givens, and that the givens are neither physical inputs nor 
ordinary objects, one is left with the view that these givens must be 
impoverished bits of information about the human world. This gives us 
the notion of "stimulus information," the sense data or snapshots intro- 
duced by Neisser. But this a priori notion of stimulus information turns 
out to be incomprehensible. All that is given empirically are continuous 
physical inputs to the organism, on the one hand, and the world of 
ordinary objects given to the perceiving subject, on the other. No cogni- 
tive psychologist has succeeded in defining another sort of input between 
these two which would provide the ultimate bits of information to which 
the rules are to be applied. All accounts offered thus far turn out to be 
an incoherent mixture of physical description in terms of energy, and 
phenomenalist description in terms of crudely defined sense data. 

Thus the psychological claim that, appearances notwithstanding, in- 
telligent behavior is produced by following fixed formal rules like a 
digital computer is stuck with a regress of rules for applying rules. It can 
not extricate itself from this regress by appeal to a notion of physical 
input which it cannot use or stimulus input which it cannot define. 

Although there is no empirical evidence either from psychology or 
from the success of current work, AI workers, like workers in CS, are 
confident that a formalization of intelligent behavior must be possible. 
Their argument is never explicitly stated, but it seems to be based on an 
ontological assumption that the world can be analyzed into independent 



What Computers Can't Do / 200 

logical elements and an epistemological assumption that our understand- 
ing of the world can then be reconstructed by combining these elements 
according to heuristic rules. The first claim is safe enough. Since he is 
not committed to describing human beings, the AI worker, unlike the 
cognitive psychologist, has no trouble identifying the ultimate bits to 
which the rules must be applied they are digitalized sound waves and 
the elements in the mosaic of a TV tube. These can be recognized without 
appeal to further rules. But the second claim, that these elements can be 
reassembled, when put forward as an a priori necessity, runs into a 
regress of higher and higher order rules, the converse of the regress of 
rules for applying rules faced by those in Cognitive Simulation. 

Since each of the logical elements is assumed to be independent of all 
the others, it has no significance until related to the other elements. But 
once these elements have been taken out of context and stripped of all 
significance it is not so easy to give it back. The significance to be given 
to each logical element depends on other logical elements, so that in 
order to be recognized as forming patterns and ultimately forming ob- 
jects and meaningful utterances each input must be related to other 
inputs by rules. But the elements are subject to several interpretations 
according to different rules and which rule to apply depends on the 
context. For a computer, however, the context itself can only be recog- 
nized according to a rule. 

Here again, too, this computer-dictated analysis conflicts with our 
experience. A phenomenological description of our experience of being- 
in-a-situation suggests that we are always already in a context or situa- 
tion which we carry over from the immediate past and update in terms 
of events that in the light of this past situation are seen to be significant. 
We never encounter meaningless bits in terms of which we have to 
identify contexts, but only facts which are already interpreted and which 
reciprocally define the situation we are in. Human experience is only 
intelligible when organized in terms of a situation in which relevance and 
significance are already given. This need for prior organization reappears 
in AI as the need for a hierarchy of contexts in which a higher or broader 
context is used to determine the relevance and significance of elements 
in a narrower or lower context. 



Conclusion / 201 

Thus, for example, to note the relevance of and to disambiguate objects 
and utterances involving knives it is necessary to know whether one is 
in a domestic, medical, or combative context (among others). It is only 
in such contexts that the presence of knives becomes relevant and signifi- 
cant. Once the context is established, it can be used to interpret the 
objects or utterances so as to determine subcontexts. For example, the 
presence of knives in a domestic context will normally establish a nour- 
ishment subcontext where objects and utterances can be disambiguated 
as having to do with eating rather than aggression. But if each context 
can only be recognized in terms of features selected as relevant and 
interpreted in terms of a broader context, the AI worker is faced with 
a regress of contexts. 

As in the case of Cognitive Simulation, there might have been an 
empirical way out of the regress. Just as for CS the ultimate uninter- 
preted bits might have been digitalized physical inputs, here the ultimate 
context or set of contexts might have been recognizable in terms of 
certain patterns or objects which had a fixed significance and could be 
used to switch the program to the appropriate subcontext of objects or 
discourse. But again as in CS the evidence is against this empirical 
possibility. There do not seem to be any words or objects which are 
always relevant and always have the same significance the way the red 
spot of a female stickleback always means mating time to the male. 

There remains only one possible "solution." The computer program- 
mer can make up a hierarchy of contexts and general rules for how to 
organize them for the computer. He does this by appealing to his general 
sense of what is generally relevant and significant for a human being. In 
some situations, however, any fact may become important. To formalize 
this so that the computer could exhibit human flexibility, the program- 
mer would have to be able to make explicit all that he normally takes 
for granted in being a human being. However, once he tries to treat his 
own situation as if he were a computer looking at it from the outside, 
the computer programmer is himself faced with an infinity of meaning- 
less facts whose relevance and significance could only be determined in 
a broader context. 

Thus it turns out that a logical atomist ontology does not entail a 



What Computers Can't Do / 202 

logical atomist epistemology. Even if the world is scanned into the 
computer in terms of logically independent bits, this does not mean that 
one can argue a priori that it can be reassembled. In fact the attempt to 
argue a priori that because the world can be resolved into bits it can be 
interpreted by formal rules ends up showing just the opposite. 

These considerations are supported by a general theory of human 
experience as being-already-in-a-situation in which the facts are always 
already interpreted. This theory also suggests that the ultimate situation 
in which human beings find themselves depends on their purposes, which 
are in turn a function of their body and their needs, and that these needs 
are not fixed once and for all but are interpreted and made determinate 
by acculturation and thus by changes in human self-interpretation. Thus 
in the last analysis we can understand why there are no facts with built-in 
significance and no fixed human forms of life which one could ever hope 
to program. 

This is not to say that children do not begin with certain fixed re- 
sponses in fact, if they did not, learning could never get started but 
rather that these responses are outgrown or overridden in the process of 
maturation. Thus no fixed responses remain in an adult human being 
which are not under the control of the significance of the situation. 

Could we then program computers to behave like children and boot- 
strap their way to intelligence? This question takes us beyond present 
psychological understanding and present computer techniques. In this 
book I have only been concerned to argue that the current attempt to 
program computers with fully formed Athene-like intelligence runs into 
empirical difficulties and fundamental conceptual inconsistencies. 
Whether a child computer could begin with situation-free responses and 
gradually learn depends on the role indeterminate needs and ability to 
respond to the global context play in learning. What work has been done 
on learning by Piaget, for example, suggests that the same forms of 
"information processing" are required for learning which are required 
for mature intelligent behavior, and that intelligence develops by "con- 
ceptual revolutions." This should not surprise us. Computers can only 
deal with facts, but man the source of facts is not a fact or set of facts, 



Conclusion / 203 

but a being who creates himself and the world of facts in the process of 
living in the world. This human world with its recognizable objects is 
organized by human beings using their embodied capacities to satisfy 
their embodied needs. There is no reason to suppose that a world orga- 
nized in terms of these fundamental human capacities should be accessi- 
ble by any other means. 

The Future of Artificial Intelligence 

But these difficulties give us no idea of the future of artificial intelligence. 
Even if the attempt to program isolated intelligent activities always 
ultimately requires the programming of the whole mature human form 
of life, and even if an Athene-like digital computer is impossible in 
principle that is, even if mature human intelligence is organized in 
terms of a field which is reciprocally determined by the objects in it and 
capable of radical revision the question still remains to what extent 
workers in artificial intelligence can use their piecemeal techniques to 
approximate intelligent human behavior. In order to complete our analy- 
sis of the scope and limits of artificial reason we must now draw out the 
practical implications of the foregoing arguments. 

Before drawing our practical conclusions, however, it will be helpful 
to distinguish four areas of intelligent activity. We can then determine 
to what extent intelligent behavior in each area presupposes the four 
human forms of "information processing" we distinguished in Part I. 
This will enable us to account for what success has been attained and 
predict what further progress can be expected. 

One can distinguish four types of intelligent activity (see Table 1). We 
have seen that the first two types are amenable to digital computer 
simulation, while the third is only partially programmable and the fourth 
is totally intractable. 

Area I is where the S-R psychologists are most at home. It includes 
all forms of elementary associationistic behavior where meaning and 
context are irrelevant to the activity concerned. Rote learning of non- 
sense syllables is the most perfect example of such behavior so far pro- 
grammed, although any form of conditioned reflex would serve as well. 



What Computers Can't Do / 204 

Table 1 
CLASSIFICATION OF INTELLIGENT ACTIVITIES 



I. Associationistic 



II. Simple Formal I III. Complex Formal I IV. Nonfonnal 



Characteristics of Activity 



Irrelevance of mean- 


Meanings completely 


In principle, same as 


Dependent on 


ing and situation. 


explicit and situation 


II; in practice, in- 


meaning and 




independent. 


ternally situation- 


situation which 






dependent, indepen- 


are not explicit. 






dent of external 








situation. 




Innate or 


Learned by rule. 


Learned by rule and 


Learned by per- 


learned by 




practice. 


spicuous examples. 


repetition. 









Field of Activity (and Appropriate Procedure) 



Memory games, e.g., 


Computable or quasi- 


Uncomputable 


Ill-defined games, 


"Geography" (asso- 


computable games, 


games, e.g., chess or 


e.g., riddles (percep- 


ciation). 


e.g., trim or tic-tac- 


go (global intuition 


tive guess). 




toe (seek algorithm 


and detailed count- 






or count out). 


ing out). 




Maze problems 


Combinatorial prob- 


Complex combina- 


Open-structured 


(trial and error). 


lems (nonheuristic 


torial problems 


problems (insight). 




means/ends analysis). 


(planning and maze 








calculation). 




Word-by-word 


Proof of theorems 


Proof of theorems 


Translating a 


translation 


using mechanical 


where no mechanical 


natural language 


(mechanical 


proof procedures 


proof procedure 


(understanding in 


dictionary) . 


(seek algorithm). 


exists (intuition and 


context of use). 






calculation). 




Response to rigid 


Recognition of sim- 


Recognition of com- 


Recognition of 


patterns (innate 


ple rigid patterns, 


plex patterns hi 


varied and distorted 


releasers and classi- 


e.g., reading typed 


noise (search for 


patterns (recogni- 


cal conditioning). 


page (search for 


regularities). 


tion of generic or 




traits whose con- 




use of paradigm 




junction defines class 




case). 




membership). 







Kinds of Program 



Decision tree, 
list search, 
template. 


Algorithm. 


Search-pruning 
heuristics. 


None. 



Conclusion / 205 

Also some games, such as the game sometimes called Geography (which 
simply consists of finding a country whose name begins with the last 
letter of the previously named country), belong in this area. In language 
translating, this is the level of the mechanical dictionary; in problem 
solving, that of pure trial-and-error search routines; in pattern recogni- 
tion, matching pattern against fixed templates. 

Area II is the domain of Pascal's esprit degeometrie the terrain most 
favorable for artificial intelligence. It encompasses the conceptual rather 
than the perceptual world. Problems are completely formalized and 
completely calculable. For this reason, it might best be called the area 
of the simple-formal. Here artificial intelligence is possible in principle 
and in fact. 

In Area II, natural language is replaced by a formal language, of which 
the best example is logic. Games have precise rules and can be calculated 
out completely, as in the case of nim or tic-tac-toe. Pattern recognition 
on this level takes place according to determinate types, which are 
defined by a list of traits characterizing the individuals which belong to 
the class in question. Problem solving takes the form of reducing the 
distance between means and ends by repeated application of formal rules. 
The formal systems in this area are simple enough to be manipulated by 
algorithms which require no search procedure at all (for example, 
Wang's logic program). Heuristics are not 01 j unnecessary here, they 
are a positive handicap, as the superiority of Wang's algorithmic logic 
program over Newell, Shaw, and Simon's heuristic logic program dem- 
onstrates. In this area, artificial intelligence has had its only unqualified 
successes. 

Area III, complex-formal systems, is the most difficult to define and 
has generated most of the misunderstandings and difficulties in the field. 
It contains behavior which is in principle reproducible but in fact intract- 
able. As the number of elements increases, the number of transforma- 
tions required grows exponentially with the number of elements 
involved. As used here, "complex-formal" includes those systems which 
in practice cannot be dealt with by exhaustive enumeration algorithms 
(chess, go, etc.), and thus require heuristic programs. 1 * 

Area IV might be called the area of nonformal behavior. This includes 



What Computers Can't Do / 206 

all those everyday activities in our human world which are regular but 
not rule governed. The most striking example of this controlled impreci- 
sion is our disambiguation of natural languages. This area also includes 
games in which the rules are not definite, such as guessing riddles. 
Pattern recognition in this domain is based on recognition of the generic, 
or of the typical, by means of a paradigm case. Problems on this level 
are open-structured, requiring a determination of what is relevant and 
insight into which operations are essential, before the problem can be 
attacked. 2 * Techniques on this level are usually taught by generalizing 
from examples and are followed intuitively without appeal to rules. We 
might adopt Pascal's terminology and call Area IV the home of the esprit 
de finesse. Since in this area a sense of the global situation is necessary 
to avoid storing an infinity of facts, it is impossible in principle to use 
discrete techniques to reproduce directly adult behavior. Even to order 
the four as in Table 1 is misleadingly encouraging, since it suggests that 
Area IV differs from Area III simply by introducing a further level of 
complexity, whereas Area IV is of an entirely different order than Area 
III. Far from being more complex, it is really more primitive, being 
evolutionarily, ontogenetically, and phenomenologically prior to Areas 
II and III, just as natural language is prior to mathematics. 

The literature of artificial intelligence generally fails to distinguish 
these four areas. For example, Newell, Shaw, and Simon announce that 
their logic theorist "was devised to leam how it is possible to solve 
difficult problems such as proving mathematical theorems [II or III], 
discovering scientific laws from data [III and IV], playing chess [III], or 
understanding the meaning of English prose [IV]." 3 The assumption, 
made explicitly by Paul Armer of the RAND Corporation, that all 
intelligent behavior is of the same general type, has encouraged workers 
to generalize from success in the two promising areas to unfounded 
expectation of success in the other two. 

This confusion has two dangerous consequences. First there is the 
tendency, typified by Simon, to think that heuristics discovered in one 
field of intelligent activity, such as theorem proving, must tell us some- 
thing about the "information processing" in another area, such as the 
understanding of a natural language. Thus, certain simple forms of infor- 



Conclusion / 207 

mation processing applicable to Areas I and II are imposed on Area IV, 
while the unique form of "information processing" in this area, namely 
that "data" are not being "processed" at all, is overlooked. The result 
is that the same problem of exponential growth that causes trouble when 
the techniques of Areas I and II are extended to Area III shows up in 
attempts to reproduce the behavior characteristic of Area IV. 4 * 

Second, there is the converse danger. The success of artificial intelli- 
gence in Area II depends upon avoiding anything but discrete, determi- 
nate, situation-free operations. The fact that, like the simple systems in 
Area II, the complex systems in Area III are formalizable leads the 
simulator to suppose the activities in Area III can be reproduced on a 
digital computer. When the difference in degree between simple and 
complex systems turns out in practice, however, to be a difference in kind 
exponential growth becoming a serious problem the programmer, 
unaware of the differences between the two areas, tries to introduce 
procedures borrowed from the observation of how human beings per- 
form the activities in Area IV for example, position evaluation in chess, 
means-ends analysis in problem solving, semantic considerations in theo- 
rem proving into Area III. These procedures, however, when used by 
human beings depend upon one or more of the specifically human forms 
of "information processing" for human beings at least, the use of chess 
heuristics presupposes fringe consciousness of a field of strength and 
weakness; the introduction of means-ends analysis eventually requires 
planning and thus a distinction between essential and inessential opera- 
tions; semantic considerations require a sense of the context. 

The programmer confidently notes that Area III is in principle formal- 
izable just like Area II. He is not aware that in transplanting the tech- 
niques of Area IV into Area III he is introducing into the continuity 
between Areas II and III the discontinuity which exists between Areas 
III and IV and thus introducing all the difficulties confronting the for- 
malization of nonformal behavior. Thus the problems which in principle 
should only arise in trying to program the "ill-structured," that is, 
open-ended activities of daily life, arise in practice for complex-formal 
systems. Since what counts as relevant data in Area III is completely 
explicit, heuristics can work to some extent (as in Samuel's Checker 



What Computers Can't Do / 208 

Program), but since Area IV is just that area of intelligent behavior in 
which the attempt to program digital computers to exhibit fully formed 
adult intelligence must fail, the unavoidable recourse in Area III to 
heuristics which presuppose the abilities of Area IV is bound, sooner or 
later, to run into difficulties. Just how far heuristic programming can go 
in Area III before it runs up against the need for fringe consciousness, 
ambiguity tolerance, essential/inessential discrimination, and so forth, is 
an empirical question. However, we have seen ample evidence of trouble 
in the failure to produce a chess champion, to prove any interesting 
theorems, to translate languages, and in the abandonment of GPS. 

Still there are some techniques for approximating some of the Area IV 
short-cuts necessary for progress in Area III, without presupposing the 
foregoing human forms of "information processing" which cannot be 
reproduced in any Athena-like program. 

To surmount present stagnation in Area III the following improved 
techniques seem to be required: 

1. Since current computers, even primitive hand-eye coordinating ro- 
bots, do not have bodies in the sense described in Chapter 7, and since 
no one understands or has any idea how to program the global organiza- 
tion and indeterminacy which is characteristic of perception and embod- 
ied skills, the best that can be hoped for at this time is some sort of crude, 
wholistic, first-level processing, which approximates the human ability 
to zero in on a segment of a field of experience before beginning explicit 
rule-governed manipulation or counting out. This cannot mean adding 
still further explicit ways of picking out what area is worth exploring 
further. In chess programs, for example, it is beginning to be clear that 
adding more and more specific bits of chess knowledge to plausible move 
generators, finally bogs down in too many ad hoc subroutines. (Samuel 
thinks this is why there has been no further progress reported for the 
Greenblatt chess program. 5 ) What is needed is something which corre- 
sponds to the master's way of seeing the board as having promising and 
threatening areas. 

Just what such wholistic processing could be is hard to determine, 
given the discrete nature of all computer calculations. There seem to be 
two different claims in the air. When Minsky and Papert talk of finding 



Conclusion / 209 

"global features," they seem to mean finding certain isolable, and deter- 
minate, features of a pattern (for example, certain angles of intersection 
of two lines) which allow the program to make reliable guesses about the 
whole. This just introduces further heuristics and is not wholistic in any 
interesting sense. Neisser, however, in discussing the problem of seg- 
menting shapes for pattern recognition before analyzing them in detail 
makes a more ambitious proposal. 

Since the processes of focal attention cannot operate on the whole visual field 
simultaneously, they can come into play only after preliminary operations have 
already segregated the figural units involved. These preliminary operations are 
of great interest in their own right. They correspond in part to what the Gestalt 
psychologists called "autochthonous forces," and they produce what Hebb called 
"primitive unity." I will call them the preattentive processes to emphasize that 
they produce the objects which later mechanisms are to flesh out and interpret. 
The requirements of this task mean that the preattentive processes must be 
genuinely "global" and "wholistic." Each figure or object must be separated from 
the others in its entirety, as a potential framework for the subsequent and more 
detailed analyses of attention. 6 

But Neisser is disappointing when it comes to explaining how this 
crude, first approximation is to be accomplished by a digital computer. 
He seems to have in mind simply cleaning-up heuristics which, as 
Neisser implicitly admits, only work where the patterns are already fairly 
clearly demarcated. "Very simple operations can separate units, provided 
they have continuous contours or empty spaces between them. Computer 
programs which follow lines or detect gaps, for example, are as easily 
written as those which fill holes and wipe out local irregularities." 7 But 
such techniques fail, for example, in the case of cursive script. 

Of course, it is hard to propose anything else. What is being asked for 
is a way of dealing with the field of experience before it has been broken 
up into determinate objects, but such preobjective experience is, by defi- 
nition, out of bounds for a digital computer. Computers must apply 
specific rules to determinate data; if the problem is one of first carving 
out the determinate data, the programmer is left with the problem of 
applying determinate rules to a blur. 

The best that can be hoped in trying to circumvent the techniques of 



What Computers Can't Do / 270 

Area IV, therefore, may well be the sort of clever heuristics Minsky and 
Papert propose to enable a first-pass program to pick out certain specific 
features which will be useful in directing the program in filling in more 
details. But such a d hoc techniques risk becoming unmanageable and in 
any case can never provide the generality and flexibility of a partially 
determinate global response. 

2. A second difficulty shows up in connection with representing the 
problem in a problem-solving system. It reflects the need for essential/ 
inessential discrimination. Feigenbaum, in discussing problems facing 
artificial intelligence research in the second decade, calls this problem 
"the most important though not the most immediately tractable." 8 He 
explains the problem as follows: 

In heuristic problem solving programs, the search for solutions within a problem 
space is conducted and controlled by heuristic rules. The representation that 
defines the problem space is the problem solver's "way of looking at" the problem 
and also specifies the form of solutions. Choosing a representation that is right 
for a problem can improve spectacularly the efficiency of the solution-finding 
process. The choice of problem representation is the job of the human program- 
mer and is a creative act. 9 

This is the activity we called finding the deep structure or insight. 
Since current computers, even current primitive robots, do not have 
needs in the sense we have discussed in Chapter 9, and since no one has 
any idea how to program needs into a machine, there is no present hope 
of dispensing with this "creative act." The best that can be expected at 
this time is the development of programs with specific objectives which 
take an active part in organizing data rather than passively receiving 
them. Programmers have noticed that, in the analysis of complex scenes, 
it is useful to have the program formulate an hypothesis about what it 
would expect to find on the basis of data it already has, and look for that. 
This should not be confused with the way the human being organizes 
what counts as data in terms of his field of purposes. All that can be 
expected is fixed rules to apply to fixed data; that is, there will be a 
programmed set of alternatives, and the program can, on the basis of 
present data, select one of these alternatives as the most probable and_ 
look for further data on the basis of this prediction. 



Conclusion / 211 

Thus, specific long-range objectives or a set of alternative long-range 
objectives might be built into game-playing and problem-solving pro- 
grams, so that in certain situations certain strategies would be tried by 
the computer (and predicted for the opponent). This technique, of 
course, would not remove the restriction that all these alternatives must 
be explicitly stored beforehand and explicitly consulted at certain points 
in the program, whereas human purposes implicitly organize and direct 
human activity moment by moment. Thus even with these break- 
throughs the computer could not exhibit the flexibility of a human being 
solving an open-structured problem (Area IV), but these techniques 
could help with complex-formal problems such as strategy in games and 
long-range planning in organizing means-ends analysis. 

3. Since computers are not in a situation, and since no one under- 
stands how to begin to program primitive robots, even those which move 
around, to have a world, computer workers are faced with a final prob- 
lem: how to program a representation of the computer's environment. 
We have seen that the present attempt to store all the facts about the en- 
vironment in an internal model of the world runs up against the prob- 
lem of how to store and access this very large, perhaps infinite amount 
of data. This is sometimes called the large data base problem. Minsky's 
book, as we have seen, presents several ad hoc ways of trying to get 
around this problem, but so far none has proved to be generalizable. 

In spite of Minsky's claims to have made a first step in solving the 
problem, C. A. Rosen in discussing current robot projects after the work 
reported in Minsky's book acknowledges new techniques are still re- 
quired: 

We can foresee an ultimate capability of storing an encyclopedic quantity of facts 
about specific environments of interest, but new methods of organization are 
badly needed which permit both rapid search and logical deductions to be made 
efficiently. 10 

In Feigenbaum's report, there is at last a recognition of the seriousness 
of this problem and even a suggestion of a different way to proceed. In 
discussing the mobile robot project at the Stanford Research Institute, 
Feigenbaum notes: 



What Computers Can't Do / 272 

It is felt by the SRI group that the most unsatisfactory part of their simulation 
effort was the simulation of the environment. Yet, they say that 90% of the effort 
of the simulation team went into this part of the simulation. It turned out to be 
very difficult to reproduce in an internal representation for a computer the 
necessary richness of environment that would give rise to interesting behavior 
by the highly adaptive robot. 11 

We have seen that this problem is avoided by human beings because their 
model of the world is the world itself. It is interesting to find work at 
SRI moving in this direction. 

It is easier and cheaper to build a hardware robot to extract what information 
it needs from the real world than to organize and store a useful model. Crudely 
put, the SRI group's argument is that the most economic and efficient store of 
information about the real world is the real world itself. 12 

This attempt to get around the large data base problem by recalculat- 
ing much of the data when needed is an interesting idea, although how 
far it can go is not yet clear. It presupposes some solution to the wholistic 
problem discussed in 1 above, so that it can segment areas to be recog- 
nized. It also would require some way to distinguish essential from 
inessential facts. Most fundamentally, it is of course limited by having 
to treat the real world, whether stored in the robot memory or read off 
a TV screen, as a set of facts; whereas human beings organize the world 
in terms of their interests so that facts need be made explicit only insofar 
as they are relevant. 

What can we expect while waiting for the development and application 
of these improved techniques? Progress can evidently be expected in 
Area II. As Wang points out, "we are in possession of slaves which are 
. . . persistent plodders." 13 We can make good use of them in the area 
of simple-formal systems. Moreover, the protocols collected by Newell, 
Shaw, and Simon suggest that human beings sometimes operate like 
digital computers, within the context of more global processes. Since 
digital machines have symbol-manipulating powers superior to those of 
humans, they should, so far as possible, take over the digital aspects of 
human "information processing/' 



Conclusion / 273 

To use computers in Areas III and IV we must couple their capacity 
for fast and accurate calculation with the short-cut processing made 
possible by fringe-consciousness, insight, and ambiguity tolerance. Leib- 
niz already claimed that a computer "could enhance the capabilities of 
the mind to a far greater extent than optical instruments strengthen the 
eyes." But microscopes and telescopes are useless without the selecting 
and interpreting eye itself. Thus a chess player who could call on a 
machine to count out alternatives once he had zeroed in on an interesting 
area would be a formidable opponent. Likewise, in problem solving, once 
the problem is structured and an attack planned, a machine could take 
over to work out the details (as in the case of machine-shop allocation 
or investment banking). A mechanical dictionary which could display 
meanings on a scope ranked as to their probable relevance would be 
useful in translation. In pattern recognition, machines are able to recog- 
nize certain complex patterns that the natural prominences in our experi- 
ence lead us to ignore. Bar-Hillel, Oettinger, and John Pierce have each 
proposed that work be done on systems which promote a symbiosis 
between computers and human beings. As Walter Rosenblith put it at 
a recent symposium, "Man and computer is capable of accomplishing 
things that neither of them can do alone." 14 

Indeed, the first successful use of computers to augment rather than 
replace human intelligence has recently been reported. A theorem-prov- 
ing program called SAM (Semi-Automated Mathematics) has solved an 
open problem in lattice theory. According to its developers: 

Semi-automated mathematics is an approach to theorem-proving which seeks to 
combine automatic logic routines with ordinary proof procedures in such a 
manner that the resulting procedure is both efficient and subject to human 
intervention in the form of control and guidance. Because it makes the math- 
ematician an essential factor in the quest to establish theorems, this approach is 
a departure from the usual theorem-proving attempts in which the computer 
unaided seeks to establish proofs. 13 

One would expect the mathematician, with his sense of relevance, to 
assist the computer in zeroing in on an area worth counting out. And 
this is exactly what happens. 



What Computers Can't Do / 214 

The user may intervene in the process of proof in a number of ways. His selection 
of the initial formulas is of course an important factor in determining the course 
AUTO-LOGIC will take. Overly large or ill-chosen sets of initial formulas tend 
to divert AUTO-LOGIC to the proving of trivial and uninteresting results so that 
it never gets to the interesting formulas. Provided with a good set of initial 
formulas, however, AUTO-LOGIC will produce useful and interesting results. 
As the user sees that AUTO-LOGIC is running out of useful ways in which to 
use the original formulas, he can halt the process and insert additional axioms 
or other material. He can also guide the process by deleting formulas which seem 
unimportant or distracting. This real-time interplay between man and machine 
has been found to be an exciting and rewarding mode of operation. 16 

Instead of trying to make use of the special capacities of computers, 
however, workers in artificial intelligence blinded by their early suc- 
cess and hypnotized by the assumption that thinking is a continuum 
will settle for nothing short of unaided intelligence. Feigenbaum and 
Feldman's anthology opens with the baldest statement of this dubious 
principle: 

In terms of the continuum of intelligence suggested by Armer, the computer 
programs we have been able to construct are still at the low end. What is 
important is that we continue to strike out in the direction of the milestone that 
represents the capabilities of human intelligence. Is there any reason to suppose 
that we shall never get there? None whatever. Not a single piece of evidence, no 
logical argument, no proof or theorem has ever been advanced which demon- 
strates an insurmountable hurdle along the continuum. 17 

Armer prudently suggests a boundary, but he is still optimistic: 

It is irrelevant whether or not there may exist some upper bound above which 
machines cannot go in this continuum. Even if such a boundary exists, there is 
no evidence that it is located close to the position occupied by today's machines. 18 

Current difficulties, once they are interpreted independently of opti- 
mistic a priori assumptions, however, suggest that the areas of intelligent 
behavior are discontinuous and that the boundary is near. The stagnation 
of each of the specific efforts in artificial intelligence suggests that there 
can be no piecemeal breakthrough to fully formed adult intelligent 
behavior for any isolated kind of human performance. Game playing, 
language translation, problem solving, and pattern recognition, each 



Conclusion / 215 

depends on specific forms of human "information processing," which are 
in turn based on the human way of being in the world. And this way of 
being-in-a-situation turns out to be unprogrammable in principle using 
presently conceivable techniques. 

Alchemists were so successful in distilling quicksilver from what 
seemed to be dirt that, after several hundred years of fruitless efforts to 
convert lead into gold, they still refused to believe that on the chemical 
level one cannot transmute metals. They did, however, produce as 
by-products ovens, retorts, crucibles, and so forth, just as computer 
workers, while failing to produce artificial intelligence, have developed 
assembly programs, debugging programs, program-editing programs, 
and so on, and the M.I.T. robot project has built a very elegant mechani- 
cal arm. 

To avoid the fate of the alchemists, it is time we asked where we stand. 
Now, before we invest more time and money on the information-process- 
ing level, we should ask whether the protocols of human subjects and the 
programs so far produced suggest that computer language is appropriate 
for analyzing human behavior: Is an exhaustive analysis of human reason 
into rule-governed operations on discrete, determinate, context-free ele- 
ments possible? Is an approximation to this goal of artificial reason even 
probable? The answer to both these questions appears to be, No. 

Does this mean that all the work and money put into artificial intelli- 
gence have been wasted? Not at all, if instead of trying to minimize our 
difficulties, we try to understand what they show. The success and subse- 
quent stagnation of Cognitive Simulation and of AI, plus the omnipres- 
ent problems of pattern recognition and natural language understanding 
and their surprising difficulty, should lead us to focus research on the 
four human forms of "information processing" which they reveal and the 
situational character of embodied human reason which underlies them 
all. These human abilities are not necessary in those areas of intelligent 
activity in which artificial intelligence has had its early success, but they 
are essential in just those areas of intelligent behavior in which artificial 
intelligence has experienced consistent failure. We can then view recent 
work in artificial intelligence as a crucial experiment disaffirming the 



What Computers Can't Do / 216 

traditional assumption that human reason can be analyzed into rule- 
governed operations on situation-free discrete elements the most im- 
portant disconfirmation of this metaphysical demand that has ever been 
produced. This technique of turning our philosophical assumptions into 
technology until they reveal their limits suggests fascinating new areas 
for basic research. 

C. E. Shannon, the inventor of information theory, sees, to some 
extent, how different potentially intelligent machines would have to be. 
In his discussion of "What Computers Should be Doing," he observes: 

Efficient machines for such problems as pattern recognition, language transla- 
tion, and so on, may require a different type of computer than any we have today. 
It is my feeling that this will be a computer whose natural operation is in terms 
of patterns, concepts, and vague similarities, rather than sequential operations 
on ten-digit numbers. 19 

We have seen that, as far as we can tell from the only being that can deal 
with such "vagueness," a "machine" which could use a natural language 
and recognize complex patterns would have to have a body so that it 
could be at home in the world. 

But if robots for processing nonformal information must be, as Shan- 
non suggests, entirely different from present digital computers, what can 
now be done? Nothing directly toward programming present machines 
to behave with human intelligence. We must think in the short run of 
cooperation between men and digital computers, and only in the long run 
of nondigital automata which, if they were in a situation, would exhibit 
the forms of "information processing" essential in dealing with our 
nonformal world. Artificial Intelligence workers who feel that some 
concrete results are better than none, and that we should not abandon 
work on artificial intelligence until the day we are in a position to 
construct such artificial men, cannot be refuted. The long reign of al- 
chemy has shown that any research which has had an early success can 
always be justified and continued by those who prefer adventure to 
patience. 20 * If one insists on a priori proof of the impossibility of success, 
it is difficult, as we have seen, to show that such research is misguided 
except by denying very fundamental assumptions common to all science. 



Conclusion / 2/7 

And one can always retort that at least the goal can be approached. If, 
however, one is willing to accept empirical evidence as to whether an 
effort has been misdirected, he has only to look at the predictions and 
the results. Even if there had been no predictions, only hopes, as in 
language translation, the results are sufficiently disappointing to be self- 
incriminating. 

If the alchemist had stopped poring over his retorts and pentagrams 
and had spent his time looking for the deeper structure of the problem, 
as primitive man took his eyes off the moon, came out of the trees, and 
discovered fire and the wheel, things would have been set moving in a 
more promising direction. After all, three hundred years after the al- 
chemists we did get gold from lead (and we have landed on the moon), 
but only after we abandoned work on the alchemic level, and worked to 
understand the chemical level and the even deeper nuclear level instead. 



Notes 



INTRODUCTION 

1. Plato, Euthyphro, VII, trans. F. J. Church (New York: Library of Liberal 
Arts), 1948, p. 7. 

2. Marvin Minsky, Computation: Finite and Infinite Machines (Englewood 
Cliffs, N.J.: Prentice-Hall, 1967), p. 106. Of course, Minsky is thinking of 
computation and not moral action. 

3. Ibid. 

4. Aristotle, Nicomachean Ethics, trans. J. A. K. Thomson as The Ethics of 
Aristotle (New York: Penguin Books, 1953), p. 75. 

5. Hobbes, Leviathan (New York: Library of Liberal Arts, 1958), p. 45. 

6. Leibniz, Selections, ed. Philip Wiener (New York: Scribner, 1951), p. 18. 

7. Ibid., p. 25 

8. Ibid., p. 15. 

9. Ibid., p. 23. 

10. Ibid., p. 48. (My italics.) 

11. George Boole, Laws of Thought, Collected Logical Works (Chicago: Open 
Court, 1940), Vol. II, p. 1. 

12. A. M. Turing, "Computing Machinery and Intelligence," reprinted in Minds 
and Machines, ed. Alan Ross Anderson (Englewood Cliffs, N.J.: Prentice- 
Hall, 1964), p. 11. 

13. Ibid., p. 13. 

14. In Chapter 5 we shall have occasion to see how this principle gives strong 
but unwarranted confidence to those working to simulate human thought 
process with digital machines. 

15. Martin Heidegger, "La fin de la philosophic et la tache de la pensee," in 
Kierkegaard vivant (Paris: Gallimard, 1966), pp. 178-179. (My translation.) 
"Philosophy has come to an end in the present epoch. It has found its place 
in the scientific view. . . . The fundamental characteristic of the scientific 
determination is that it is cybernetic. . . ." 

7227 



Notes / 222 

16. Turing, op. cit., p. 7. 

17. Ibid., p. 5. 

18. See, for example, the critical articles by Kieth Gunderson and Michael 
Scriven in Minds and Machines, cited in note 12 above. 

19. Turing, op. cit., p. 30. 

20. Claude E. Shannon, "A Chess-Playing Machine,*' reprinted in World of 
Mathematics, ed. James R. Newman (New York: Simon and Schuster, 
1956), p. 2129. 

21. Ibid., p. 2129. 

22. Allen Newell, "The Chess Machine," in The Modeling of Mind, Kenneth 
M. Sayre and Frederick J. Crosson, eds. (South Bend, Ind.r Notre Dame 
University Press, 1963), p. 89. 

23. Allen Newell, J. C. Shaw, and H. A. Simon, "Chess-Playing Programs 
and the Problem of Complexity," in Computers and Thought, Edward A. 
Feigenbaum and Julian Feldman, eds. (New York: McGraw-Hill, 1963), 
p. 48. 

24. Ibid., p. 45. 

25. Allen Newell, J. C. Shaw, and H. A. Simon, "Empirical Explorations with 
the Logic Theory Machine: A Case Study in Heuristics," in Computers and 
Thought, p. 109. 

26. Allen Newell, J. C. Shaw, and H. A. Simon, "Report on a General Problem- 
Solving Program," Proc. Int. Conf. on Information Processing (Paris: 
UNESCO, 1960), p. 257. 

27. Ibid., p. 259. 

28. Herbert A. Simon and Allen Newell, "Heuristic Problem Solving: The Next 
Advance in Operations Research," Operations Research, Vol. 6 (January- 
February 1958), p. 6. 

29. Noam Chomsky, Language and Mind (New York: Harcourt, Brace & 
World, 1968), p. v. 

30. Turing, op. cit, p. 14, cited in note 12 above. 

31. Marvin Minsky, "Machines Are More Than They Seem," Science Journal 
(October 1968), p. 3. 

32. Herbert A. Simon and Allen Newell, "Heuristic Problem Solving: The Next 
Advance in Operations Research," cited in note 28 above. 

33. W. Ross Ashby, "Review of Feigenbaum's Computers and Thought, " Jour- 
nal of Nervous and Mental Diseases. 

34. D. E. Smith, History of Mathematics (Boston: Ginn, 1925), Vol. II, p. 284. 

35. Newell, Shaw, and Simon, "Chess-Playing Programs and the Problem of 
Complexity," p. 60. 

36. Ibid., p. 45. 

37. Allen Newell, J. C. Shaw, and H. A. Simon, The Processes of Crea- 
tive Thinking, The RAND Corporation, P-1320 (September 16, 1958), 
p. 6. 

38. Ibid., p. 78. 

39. Norbert Wiener, "The Brain and the Machine (Summary)," in Dimensions 
of Mind, Sidney Hook, ed. (New York: Collier, 1961), p. 110. 



Notes / 223 

40. Michael Scriven, "The Complete Robot: A Prolegomena to Androidology," 
in Dimensions of Mind, p. 122. 

41. H. A. Simon and Peter A. Simon, "Trial and Error Search in Solving 
Difficult Problems: Evidence from the Game of Chess," Behavioral Science, 
Vol. 7 (October 1962), p. 429. 

42. For example, the abstract of the Simon and Simon article (note 41 above) 
makes no mention of the forced mates but rather concludes: "This paper 
attempts to clear away some of the mythology which surrounds the game 
of chess by showing that successful problem solving is based on a highly 
selective, heuristic 'program* rather than on prodigies of memory and in- 
sight" (p. 425). And the article itself concludes with the unjustified generali- 
zation: "The evidence suggests strongly that expert chess players discover 
combinations because their programs incorporate powerful selective heuris- 
tics and not because they think faster or memorize better than other people" 
(p. 429). The evidence honestly evaluated suggests that at best this is the case 
only in specific situations in the end game. 

43. Paul Armer, "Attitudes Toward Intelligent Machines," in Computers and 
Thought, p. 405. 

44. Fred Gruenberger, Benchmarks in Artificial Intelligence, The RAND Cor- 
poration, P-2586 (June 1962), p. 6. 

45. The glee with which this victory was announced to the computer commu- 
nity, as if the prior claims about what computers could do had thereby been 
vindicated, is echoed by Alvin Toffler on p. 1 87 of Future Shock (New York: 
Random House, 1971). The author interprets me as saying that no computer 
would ever play even amateur chess. From the full quotation it is clear that 
this is a distortion. My assertion was simply a correct report of the state of 
the art at the time (1965): "According to Newell, Shaw, and Simon them- 
selves, evaluating the Los Alamos, the IBM, and the NSS programs: 'All 
three programs play roughly the same quality of chess (mediocre) with 
roughly the same amount of computing time.* Still no chess program can 
play even amateur chess, and the world championship is only two years 
away." 

46. Seymour Papert, 9th RAND Symposium (November 7, 1966), p. 116. 

47. Donald Michie, Science Journal, Vol. 4, No. 10 (October 1968), p. 1. 

48. Eliot Hearst, "Psychology Across the Chessboard," Psychology Today (June 
1967), p. 32. 

49. The third prediction that most psychological theories would take the form 
of computer programs has indeed been partially fulfilled, although there 
are still plenty of behaviorists. But the important question here is not 
whether a certain task, impressive in itself like master play or original 
theorem proving, has been achieved, but whether what is predicted would 
be an achievement even if it came to pass. The substitution in psychology 
of computer models for behaviorist models is by no means obviously a step 
forward. The issue is complicated and requires detailed discussion (see 
Chapter 4). 

50. Papert, op. cit. r p. 117. 



Notes / 224 



Part I. Ten Years of Research in Artificial Intelligence 
(1957-1967) 

CHAPTER 1. PHASE I (1957-1962) COGNITIVE SIMULATION 

1. Anthony G. Oettinger, "The State of the Art of Automatic Language Trans- 
lation: An Appraisal," in Beitraege zur Sprachkunde und Informations 
Verarbeitung, ed. Herbert Marchl, Vol. 1, Heft 2 (Munich: Oldenbourg 
Verlage, 1963), p. 18. 

2. Yehoshua Bar-Hillei, "The Present Status of Automatic Translation of Lan- 
guages/' in Advances in Computers, F. L. Alt, ed. (New York: Academic 
Press, 1960), Vol. 1, p. 94. 

3. National Academy of Sciences, Language and Machines (Washington, D.C., 
1966), p. 29. 

4. Oettinger, op. cit. t p. 21. 

5. Ibid., p. 27. Such critical evaluations of machine translation often end with 
the comforting conclusion that at least the work added to our knowledge of 
the structure of language. But even this justification is questionable. 
Chomsky takes a dim view of this "spin-off' : ". . . an appreciable investment 
of time, energy, and money in the use of computers for linguistic research 
appreciable by the standards of a small field like linguistics has not 
provided any significant advance in our understanding of the use or nature 
of language. These judgments are harsh, but I think they are defensible. They 
are, furthermore, hardly debated by active linguistic or psycholinguistic 
researchers." (Noam Chomsky, Language and Mind [New York: Harcourt, 
Brace & World, 1968], p. 4.) 

6. Language and Machines, op. tit., p. 32. 

7. The most important papers reporting work done during this period have 
been collected in Edward A. Feigenbaum and Julian Feldman, eds., Comput- 
ers and Thought (New York: McGraw-Hill, 1963). 

8. A protocol is a verbal report of a subject in the process of solving a problem. 
Here is a typical protocol from a subject trying to solve a logic problem: 
"Well, looking at the left hand side of the equation, first we want to eliminate 
one of the sides by using rule 8. It appears too complicated to work with first. 
Now no, no, I can't do that because I will be eliminating either the Q or 
the P in that total expression. I won't do that at first. Now I'm looking for 
a way to get rid of the horseshoe inside the two brackets that appear on the 
left and right sides of the equation. And I don't see it. Yeh, if you apply rule 
6 to both sides of the equation, from there I'm going to see if .1 can apply 
rule 7." (Computers and Thought, p. 282.) 

9. H. A. Simon, Modeling Human Mental Processes, The RAND Corporation, 
P-2221 (February 20, 1961), p. 15. Not that these problems were unsolved. 
Several routine, nonheuristic, mathematical algorithms have been published 
which solve these and more complex routing problems. 



Notes / 225 

10. Allen Newell and H. A. Simon, Computer Simulation of Human Thinking, 
The RAND Corporation, P-2276 (April 20, 1961); also published in Science. 
Vol. 134 (December 22, 1961), p. 19. (My italics.) 

11. H. A. Simon, op. cit, p. 12. 

12. Marvin Minsky, "Descriptive Languages and Problem Solving," Proceed- 
ings of the 1961 Western Joint Computer Conference; reprinted in Semantic 
Information Processing, Minsky, ed. (Cambridge, Mass.: M.I.T. Press, 
1969), p. 420. 

13. Ibid., p. 420. 

14. Allen Newell, Some Problems of Basic Organization in Problem-Solving Pro- 
grams, The RAND Corporation, RM-3283-PR (December 1962), p. 4. 

15. G. W. Ernst and A. Newell, Generality and GPS, Carnegie Institute of 
Technology, January 1967, p. i. 

16. Ibid., p. 45. 

17. H. Gelernter, J. R. Hansen, and D. W. Loveland, "Empirical Explorations 
of the Geometry-Theorem Proving Machine," in Computers and Thought, 
p. 160. 

18. Oliver G. Selfridge and Ulric Neisser, "Pattern Recognition by Machine," 
in Computers and Thought, p. 238. 

19. Murray Eden, "Other Pattern-Recognition Problems and Some Generaliza- 
tions," in Recognizing Patterns: Studies in Living and Automatic Systems, 
Paul A. Kolers and Murray Eden, eds. (Cambridge, Mass.: M.LT. Press, 
1968), p. 196. 

20. Selfridge and Neisser, op. cit., p. 244. 

21. Ibid., p. 250. 

22. Leonard Uhr and Charles Vossler, "A Pattern-Recognition Program that 
Generates, Evaluates, and Adjusts its own Operations:" in Computers and 
Thought, p. 251. 

23. Laveen Kanal and B. Chandrasekaran, "Recognition, Machine Recognition 
and Statistical Approaches," Methodologies of Pattern Recognition (New 
York: Academic Press, 1969), pp. 318, 319. 

24. Vincent E. Giuliano, "How We Find Patterns," International Science and 
Technology (February 1967), p. 40. 

25. Feigenbaum and Feldman, Computers and Thought, p. 276. 

26. Ibid., p. vi. 

27. Allen Newell, "The Chess Machine," in The Modeling of Mind, Kenneth 
M. Sayre and Frederick J. Crosson, eds. (South Bend, Ind.: Notre Dame 
University Press, 1963), p. 80. 

28. Ibid., p. 80. 

29. Allen Newell and H. A. Simon, Computer Simulation of Human Thinking, 
The RAND Corporation, P-2276 (April 20, 1961), p. 15. 

30. Newell, Shaw, and Simon, "Chess-Playing Programs and the Problem of 
Complexity," in Computers and Thought, p. 47. 

31. Michael Polanyi, "Experience and Perception of Pattern," in The Modeling 
of Mind, p. 214. As far as I know, Frederick Crosson was the first to see 



Notes / 226 

the relevance of this gestaltist analysis to work in artificial intelligence. In 
the Preface to The Modeling of Mind he writes: ". . . Some human functions 
are at times performed by utilizing information or clues which are not 
explicitly or focally attended to, and this seems to mark a fundamental 
difference between such functions and the processes by which they are 
simulated by automata. The reason for this difference is that the operations 
of the digital computers which are employed as models are binary in nature. 
Consequently, the function which the machine can perform . . . must be, at 
each stage, all-or-none, i.e., sufficiently specific and explicit to be answered 
'yes' or 'no* " (p. 21). Crosson, however, does not spell out the peculiarities 
and function of this nonfocal form of awareness, so it remains unclear 
whether on his view all implicit cues could in principle be made explicit and 
what, if anything, would be lost in a model which dealt only with explicit 
cues. 

32. Newell and Simon, An Example of Human Chess Play in the Light of Chess 
Playing Programs, Carnegie Institute of Technology, August 1964, pp. 10- 
11. 

33. Ibid., p. 13. (My italics.) 

34. Ibid., p. 1 1. Newell and Simon go on: "More generally, psychology has had 
little to say about how global concepts organize behavior." This is, of course, 
incredibly provincial. Gestalt psychology talks of little else. What Newell 
and Simon mean is that the kind of psychology they prefer, i.e., the kind of 
psychology that uses a computer program as its model of explanation, has 
no way of dealing with such global processes. 

35. Ibid., p. 14. 

36. Eliot Hearst, "Psychology Across the Chessboard," Psychology Today (June 
1967), p. 35. 

37. Ibid., p. 37. 

38. Minsky notes this difficulty, but on sheer faith he supposes that there must 
be a heuristic solution: "This might be done through some heuristic tech- 
nique that can assess relevancy, or through a logic which takes such conse- 
quences into account. The trouble with the latter is that the antecedents of 
all the propositions must contain a condition about the state of the system, 
and for complex systems this becomes overwhelmingly cumbersome. Other 
systematic solutions to the problem seem about equally repellent. It is a 
problem that seems urgently to require a heuristic solution." (Semantic 
Information Processing, p. 422.) 

39. Newell, Shaw, and Simon, "Chess-Playing Programs and the Problem of 
Complexity," in Computers and Thought, p. 65. 

40. Oettinger, op. cit, p. 26, cited in note 1 above. 

41. Ibid., p. 26. 

42. In serial processing the program consists of a series of operations, each one 
depending on the results of the previous ones. In parallel processing several 
such series of computation are performed simultaneously. Parallel process- 
ing can be simulated by a serial program, but the important logical difference 



Notes / 227 

remains that in a serial program each step depends on the previous ones, 
while in a parallel program, the operations in each series are independent of 
the operations in any other series. 

43. Ludwig Wittgenstein, The Blue and Brown Books (Oxford, Eng.: Basil 
Blackwell, 1960), p. 25. The participants in The RAND symposium on 
"Computers and Comprehension*' suggest the psychological basis and ad- 
vantage of this nonrulelike character of natural languages. "It is crucial that 
language is a combinatory repertoire with unlimited possible combinations 
whose meanings can be inferred from a finite set of 'rules' governing the 
components' meaning. (The so-called 'rules' are learned as response sets and 
are only partly formalizable.)" (M. Kochen, D. M. MacKay, M. E. Maron, 
M. Scriven, and L. Uhr, Computers and Comprehension, The RAND Corpo- 
ration, RM-4065-PR [April 1964], p. 12.) 

44. Bar-Hillel, op. cit., pp. 105, 106, cited in note 2 above. 

45. Edward Feigenbaum, "The Simulation of Verbal Learning Behavior,'* in 
Computers and Thought, p. 298. 

46. Marvin Minsky, "Steps Toward Artificial Intelligence," in Computers and 
Thought, p. 447. 

47. Michael Scriven, Primary Philosophy (New York: McGraw-Hill, 1966), 
p. 186. 

48. Wittgenstein, Philosophical Investigations (Oxford, Eng.: Basil Blackwell, 
1953), p. 227. Wittgenstein is here talking about how we learn to judge an 
expression of feeling, but his point is more general. 

49. Allen Newell and H. A. Simon, "GPS: A Program That Simulates Human 
Thought," in Computers and Thought, p. 288. 

50. Ibid., p. 289. 

51. Ibid., p. 290. The arbitrary nature of this ad hoc explanation is evident from 
the context. Moreover, when questioned on this point at his 1968 Mellon 
Lecture at M.I.T., Simon answered that he did not believe that parallel 
processing played a role in cognitive processes, and did not remember ever 
having held that it did. 

52. Ibid., p. 291. 

53. Ibid., p. 292. 

54. Ibid., p. 292. 

55. Max Wertheimer, Productive Thinking (New York: Harper & Bros., 1945), 
p. 202. 

56. Marvin Minsky, "Descriptive Languages and Problem Solving," in Semantic 
Information Processing, p. 421, cited in note 12 above. 

57. Newell, Shaw, and Simon, The Processes of Creative Thinking, The RAND 
Corporation, P-1320 (September 16, 1958), pp. 43-44. 

58. George Miller, Eugene Galanter, and Karl H. Pribram, Plans and the Struc- 
ture of Behavior (New York: Holt, Rinehart and Winston, 1960), pp. 179- 
180. 

59. Ibid., p. 180. 

60. Ibid., p. 191. 



Notes / 228 

61. Ibid., p. 190. (My italics.) 

62. Wertheimer, op. cit., p. 195, cited in note 55 above. 

63. Hearst, op. cit., p. 32, cited in note 36 above. 

64. Minsky, "Descriptive Languages and Problem Solving," in Semantic Infor- 
mation Processing, p. 420, cited in note 12 above. 

65. Ibid., p. 123. 

66. Edward Feigenbaum, ''Artificial Intelligence: Themes in the Second 
Decade," IFIP Congress 1968, Supplement, p. J-15. 

67. Whatever information processing the human brain employs to pick out 
patterns, this work is no doubt aided by the organization of human receptors. 
But even if organization of the input into perceptual prominences (figure and 
ground) could be built into the receptors of a digital machine, such selective 
receptors would amount to introducing a stage of analogue processing, 
which workers in artificial intelligence are committed to avoid. 

68. Oliver G. Selfridge and Ulric Neisser, "Pattern Recognition by Machine," 
in Computers and Thought, p. 238. 

69. Earl Hunt, Computer Simulation: Artificial Intelligence Studies and their 
Relevance to Psychology (Brown and Farber, 1968), p. 145. 

70. Selfridge and Neisser, "Pattern Recognition by Machine," in Computers and 
Thought, p. 238. 

71. Aron Gurwitsch, "On the Conceptual Consciousness," in The Modeling of 
Mind, p. 203. 

72. Ibid., pp. 204-205. 

73. Maurice Merleau-Ponty, Phenomenology of Perception (London: Routledge 
& Kegan Paul, 1962), pp. 128 ff. 

74. Wittgenstein, The Blue and Brown Books* p. 25. 

75. Of course, it only looks like "narrowing down" or "dw-ambiguation" to 
someone who approaches the problem from the computer's point of view. 
We shall see later that for a human being the situation is structured in terms 
of interrelated meanings so that the other possible meanings of a word or 
utterance never even have to be eliminated. They simply do not arise. 

76. Cited in Merleau-Ponty, Sense and Non-Sense, (Evanston, 111.: Northwest- 
ern University Press, 1964), p. 54. 

77. Wittgenstein, Philosophical Investigations, p. 583. 

78. Wittgenstein, Philosophical Investigations, p. 32. 

79. Since typicality, unlike classification, depends on comparison with specific 
cases, such resemblance must be fairly concrete. Thus we can speak of a 
typical Indian, but not a typical man. 

80. An interesting attempt to overcome this all-or-nothing character of class 
membership has been made by L. A. Zadeh. (See, for example, "Fuzzy Sets," 
Information and Control Vol. 8, No. 3 [June 1965].) But Zadeh's work, 
although interesting, still defines classes in terms of specific traits, merely 
allowing class members to be defined in terms of degree of membership in 
the class. "A fuzzy set is a class of objects with a continuum of grades of 
membership" (p. 338). Moreover, as Zadeh uses it, fuzziness is itself a fuzzy 



Notes / 229 

concept. Under fuzziness, Zadeh indiscriminately lumps five different pat- 
tern recognition problems: vagueness of boundary, context-dependence, 
purpose-dependence, dependence on subjective evaluation, and family 
resemblance. Thus it is never clear just which problem, if any, the formaliza- 
tion of fuzziness is supposed to solve. 

81. Wittgenstein, Philosophical Investigations, p. 32. 

82. This analysis is worked out in detail by Renford Bambrough, cf. "Universals 
and Family Resemblances," in Wittgenstein: The Philosophical Investigations 
(New York: Anchor, 1966). 

83. Ibid., p. 49. 

84. Alvin Toffler's Future Shock provides an excellent illustration of this first 
step fallacy. (See note 2 of Chapter 2.) 

85. Herbert Simon, The Shape of Automation for Men and Management (New 
York: Harper & Row, 1965), p. 96. 

CHAPTER 2. PHASE II (1962-1967) SEMANTIC INFORMATION 
PROCESSING 

1. Marvin Minsky, ed., Semantic Information Processing (Cambridge, Mass.: 
M.I.T. Press, 1969), pp. 6, 7. 

2. For example, the following report in the Chicago Tribune of June 7, 1963: 
"The development of a machine that can listen to any conversation and type 
out the remarks just like an office secretary was announced yesterday by a 
Cornell University expert on learning machines. The device is expected to 
be in operation by fall [sic]. Frank Rosenblatt, director of Cornell's cognitive 
systems research, said the machine will be the largest 'thinking' device built 
to date. Rosenblatt made his announcement at a meeting on learning ma- 
chines at Northwestern University's Technological Institute." 

In their mathematical study, Perceptrons (Cambridge, Massachusetts: 
M.LT. Press, 1969), Minsky and Papert arrive at a much less optimistic 
evaluation of perceptron work: "Perceptrons have been widely publicized as 
'pattern recognition* or 'learning* machines and as such have been discussed 
in a large number of books, journal articles, and voluminous 'reports.' Most 
of this writing ... is without scientific value. ... [p. 4]. 

"Rosenblatt's [1958] schemes quickly took root, and soon there were 
perhaps as many as a hundred groups, large and small, experimenting with 
the model either as a 'learning machine* or in the guise of 'adaptive* or 
'self-organizing* networks or 'automatic control* systems. The results of 
these hundreds of projects and experiments were generally disappointing, 
and the explanations inconclusive. The machines usually work quite well on 
very simple problems but deteriorate very rapidly as the tasks assigned to 
them get harder" [p. 9]. 

In the light of these practical difficulties and the theoretical limitations 
Minsky and Papert demonstrate, enthusiasm about the future of Perceptrons 
is a perfect illustration of the first step fallacy. (See note 84 above.) Typical 
of this falacious extrapolation is Toffler's claim {Future Shock, p. 186) that: 



Notes / 230 

"Experiments by ... Frank Rosenblatt and others demonstrate that ma- 
chines can learn from their mistakes, improve their performance, and in 
certain limited kinds of learning, outstrip human students." Toffler gives no 
indication of the seriousness of these limitations. 

3. Minsky, Semantic Information Processing, p. 7. 

4. Ibid., p. 8. 

5. Minsky, "Descriptive Languages and Problem Solving," in Semantic Infor- 
mation Processing, p. 419. 

6. Minsky, Semantic Information Processing, pp. 78. 

7. Ibid., p. 5. 

8. Minsky, "Artificial Intelligence," Scientific American, Vol. 215, No. 3 (Sep- 
tember 1966), p. 257. 

9. Daniel G. Bobrow, "Natural Language Input for a Computer Problem 
Solving System" in Semantic Information Processing, p. 135. 

10. Ibid., p. 137. 

11. Minsky, Semantic Information Processing, p. 18. 

12. Ibid., p. 20. 

13. Bobrow, op. cit., p. 183. 

14. Daniel Bobrow, "Natural Language Input for a Computer Problem Solving 
Program," MAC-TR-1, M.I.T., abstract of thesis, p. 3. (My italics.) 

15. Bobrow, "Natural Language Input for a Computer Problem Solving Sys- 
tem," in Semantic Information Processing, p. 135. 

16. Ibid., p. 144. 

17. Ibid., p. 191. 

18. Ibid., p. 135. In the sense of "understands" and "English" used by Bobrow, 
a machine which did nothing more than, when told, "You are on," answered 
the question "Are you on?" with "Yes," would "understand English." 

19. Ibid., p. 194. 

20. Minsky, Semantic Information Processing, p. 14. 

21. Bobrow, op. cit, in Semantic Information Processing, p. 192. 

22. Minsky, "Artificial Intelligence," p. 260, cited in note 8 above. 

23. Bobrow, Semantic Information Processing, p. 194. 

24. Bobrow, Natural Language Input, p. 3, cited in note 14 above. 

25. In his Scientific American article (p. 258), Minsky asks: "Why are the 
programs not more intelligent than they are?" and responds, ". . . until 
recently resources in people, time and computer capacity have been quite 
limited. A number of the more careful and serious attempts have come close 
to their goal . . . others have been limited by core memory capacity; still 
others encountered programming difficulties." 

26. Thomas G. Evans, "A Program for the Solution of a Class of Geometric- 
Analogy Intelligence Test Questions," in Semantic Information Processing, 
pp. 346-347. 

27. Ibid., p. 349. 

28. Ibid., p. 350. 

29. Ibid. 



Notes / 231 

30. Minsky, Semantic Information Processing, p. 16. (My italics.) 

31. Minsky, "Artificial Intelligence," p. 250. (My italics.) 

32. Evans, op. cit., p. 280. (My italics.) 

33. Rudolf Arnheim, "Intelligence Simulated," Midway, University of Chicago 
(June 1967), pp. 85-87. 

34. Ibid. 

35. Unlike Minsky, Simon, although one of the faithful, does not seem to require 
a public profession of faith on the part of his Ph.D. students. 

36. Ross Quillian, "Semantic Memory," in Semantic Information Processing, 
p. 251. 

37. Ross Quillian, Semantic Memory, Bolt, Beranek and Newman, Inc., paper 
AFCRL-66-189 (October 1966), p. 54. (Omitted in Minsky's condensed 
version of the thesis.) 

38. Quillian, "Semantic Memory,'* in Semantic Information Processing, pp. 
219-220. 

39. Ibid., p. 221. 

40. Ibid., p. 222. 

41. Ibid., p. 216. 

42. Ibid., p. 247. 

43. Quillian, Semantic Memory, p. 113, cited in note 37 above. 

44. Quillian, "Semantic Memory," in Semantic Information Processing, p. 235. 

45. Ibid., p. 235. 

46. Ibid., p. 235. 

47. Ibid., p. 241. 

48. Minsky, Semantic Information Processing, p, 1. 

49. Ibid., p. 26. 

50. Ibid., p. 18. 

51. Marvin Minsky, Computation: Finite and Infinite Machines (Englewood 
Cliffs, N.J.: Prentice-Hall, 1967), p. 2. 

52. Minsky, Semantic Information Processing, p. 13. 

53. Ibid., p. 13. 

54. Ibid., p. 13. (My italics.) 

55. Ibid., p. 26. 

56. Bar-Hillel, "Critique of June 1966 Meeting," SIGART Newsletter, p. 1. 

CONCLUSION 

1. Minsky, "Artificial Intelligence," p. 258, cited in note 8 above. 

2. R. J. Solomonoff, "Some Recent Work in Artificial Intelligence," Proceed- 
ings of the IEEE, Vol. 54, No. 12 (December 1966), p. 1689. 

3. Ibid., p. 1691. 

4. Ibid., p. 1693. 

5. Ibid., p. 1693. 

6. Fred M. Tonge, "A View of Artificial Intelligence," Proceedings, A.C.M. 
National Meeting (1966), p. 379. 

7. P. E. Greenwood, Computing Reviews (January-February 1967), p. 31. 



Notes / 232 

Part II. Assumptions Underlying Persistent Optimism 

INTRODUCTION 

1. Allen Newell and H. A. Simon, Computer Simulation of Human Thinking, 
The RAND Corporation, P-2276 (April 20, 1961), p. 9. (My italics.) 

CHAPTER 3. THE BIOLOGICAL ASSUMPTION 

1. It should be noted, for the discussion at hand, that even if restricted types 
of perceptrons can be shown to be incapable of recognition and learning (See 
note 2 of Chapter 2.) the theoretical possibility that a neural net of sufficient 
complexity could learn is not excluded. This possibility must constantly be 
borne in mind in evaluating the arguments for the biological and psychologi- 
cal assumptions that the brain or mind must function like a heuristically 
programmed digital computer. 

2. John von Neumann, Probabilistic Logics and the Synthesis of Reliable Orga- 
nisms from Unreliable Components, Collected Works, A. H. Taub, ed. (New 
York: Pergamon Press, 1963), Vol. 5, p. 372. (My italics.) 

3. John von Neumann, "The General and Logical Theory of Automata," 
reprinted in The World of Mathematics (New York: Simon and Schuster, 
1956), p. 2077. 

4. I am grateful to Walter M. Elsasser and R. L. Gregory for having helped 
me formulate this distinction. 

5. Theodore H. Bullock, "Evolution of Neurophysiological Mechanisms," in 
Behavior and Evolution, Anne Roe and George Gaylord Simpson, eds. (New 
Haven, Conn.: Yale University Press, 1958), p. 172. 

6. Jerome Lettvin, lecture at the University of California, Berkeley, November 
1969. 

7. Walter A. Rosenblith, "On Cybernetics and the Human Brain," The Ameri- 
can Scholar (Spring 1966), p. 247. 

CHAPTER 4. THE PSYCHOLOGICAL ASSUMPTION 

1. Ulric Neisser, Cognitive Psychology (New York: Appleton-Century-Crofts, 
1967), p. 6. 

2. Miller, Galanter, and Pribram, Plans and the Structure of Behavior (New 
York: Holt, Rinehart and Winston, 1960), p. 57. 

3. Claude E. Shannon, "The Mathematical Theory of Communication," in The 
Mathematical Theory of Communication, Claude E. Shannon and Warren 
Weaver (Urbana: University of Illinois Press, 1962), p. 3. 

4. Warren Weaver, "Recent Contributions to the Mathematical Theory of 
Communication," in The Mathematical Theory of Communication, p. 99, 
cited in note 3 above. 



Notes / 233 

5. In this context, Newell, Shaw, and Simon's claim to have synthesized the 
contributions of behaviorists and Gestaltists by, on the one hand, accepting 
behavioral measures, and, on the other, recognizing that "a human being is 
a tremendously complex, organized system" ("GPS: A Program that Simu- 
lates Human Thought," pp. 280, 293) shows either a will to obscure the 
issues or a total misunderstanding of the contribution of each of these 
schools. 

6. See Part III. 

7. Jerry A. Fodor, "The Appeal to Tacit Knowledge in Psychological Explana- 
tion," The Journal of Philosophy, Vol. No. 20 (October 24, 1968), p. 632. 

8. Ibid., p. 629. 

9. Ibid., p. 637. 

10. Jerry Fodor, Psychological Explanation (New York: Random House, 1968), 
p. 138. 

1 1. The other reading of the simulability claim, the reading which is relevant to 
the mentalist's purposes but, unfortunately, lacks the immediate credibility 
of the first, is that any analog processor can also be represented. The flaw 
in this alternative, however, is difficult to grasp until a few examples have 
clarified the distinction between simulation and representation. The division 
function of a slide rule is simulated by any algorithm which yields appropri- 
ate quotients; but it is represented only if the quotients are obtained in a 
sliderulelike manner in which the steps correspond to comparing lengths. 
On a^computer this would amount to assigning (colinear) spatial coordinates 
to the mantissas of two log tables, and effecting a "translation" by subtract- 
ing. To treat a more general case, one can simulate any multiply coupled 
harmonic system (such as most commercial analogue computers) by solving 
their characteristic differential equations. On the other hand, a representa- 
tion, roughly a simulation of the inner operation as well as the end result, 
would require a simulation of each electronic component (resistors, capaci- 
tors, wires, etc.), their effects on one another, and thence their variations 
iterated through time. 

Each of these analogues happens to be both simulable and representable, 
but this is not always the case. Some analogues are not composed of identifia- 
ble parts, e.g., a soap film "computing" the minimum surface which is 
bounded by an irregularly shaped wire, and hence are not representable in 
anything like the above fashion. 

Now it might be claimed that since a soap bubble (or any other material 
object) is made of atoms it can still always be represented in principle by 
working out an immense (!) amount of quantum mechanics. But it is at best 
very dubious that such a mountain of equations would or could amount to 
an explanation of how something works, or in the case of the brain, have any 
relevance at all to psychology. If this needs to be any more obvious than it 
is, think of an ordinary adding machine that works with wheels and cogs; 
our conviction that it works mechanically and can be represented in every 
interesting sense is not in the least based on the fact that it is made of atoms. 
In fact, it could be made of some totally mysterious, indivisible substance 



Notes / 234 

and everyone would remain confident that insofar as it worked with the 
wheels, cogs, and all, it would still be a mechanism, and any representation 
in terms of the wheels and cogs would count as an explanation. Essentially 
the same point could be made about electronic analogue computers, slide 
rules, and so on. 

Thus, the plausibility of the a priori position that an analogue can always 
be digitally represented is illegitimate, only borrowed, so to speak, from the 
plausibility of the much weaker and irrelevant claim of mere simulability. 

12. Miller, Galanter, and Pribram, op. cit, p. 16. (My italics.) 

13. Newell and Simon, "GPS: A Program that Simulates Human Thought," 
p. 293. 

14. Newell and Simon, Computer Simulation of Human Thinking, p. 9. 

15. Ibid., p. 292. 

16. Thomas Kuhn, The Structure of Scientific Revolutions (Chicago: University 
of Chicago Press, 1962), p. 81. 

17. Ibid., p. 17. 

18. Ibid., p. 81. 

19. Newell and Simon, Computer Simulation of Human Thinking, p. 9. 

20. Herbert Simon and Alan Newell, "Information Processing in Computer and 
Man," American Scientist, Vol. 52 (September 1964), p. 282. 

21. Miller, Galanter, and Pribram, op. cit. p. 16. (My italics.) 

22. See Plato, Meno. 

23. Introduction, Section I. 

24. Miller et al., op. cit., p. 17. Or compare Minsky in his article "Artificial 
Intelligence," Scientific American, Vol. 215, No. 3 (September 1966): 
"Evans began his work ... by proposing a theory of the steps or processes 
a human brain might use in dealing with such a situation" (p. 250). Again, 
Minsky and Papert direct their book Perceptrons (M.I.T. Press, 1969) to 
"psychologists and biologists who would like to know how the brain com- 
putes thoughts" (p. 1). Quillian in his thesis, Semantic Memory, says, 
". . . to understand such meaning is either to find or to create in the brain 
of the understander some configuration of symbols. . . ." (p. 70). 

25. Jerry Fodor, Psychological Explanation , p. 30. 

26. Ibid., p. 22. 

27. Neisser, op. cit., p. 3. 

28. Of course, phenomenologically, it is objects, not light waves we have direct 
access to. 

29. Neisser, op. cit, p. 3. 

30. Ibid., p. 3. (My italics.) 

31. Unless one adopts the identity theory of sensations and brain states which 
Neisser does not seem to hold, since it would require a further justification 
which Neisser nowhere gives. 

32. Neisser, op. cit. t p. 4. (My italics.) 

33. Ibid., p. 5. "Our knowledge of the world must be somehow developed from 
the stimulus input. . . ." 



Notes / 235 

34. Ibid., p. 22. 

35. Rather than revive the Humean notion of sense data and then find oneself 
forced to introduce Kantian rules to account for their combination into the 
perception of objects, it would be more illuminating, and presumably a better 
guide to research, to determine what psychologists such as Neisser actually 
do, regardless of their mistaken conceptualization. Such work involves try- 
ing to find those cues in the perceptual field which are significant in various 
areas of perception; for example, those cues which are essential in our 
perception of depth. One can find out which cues are necessary by systemati- 
cally excluding various factors such as binocular vision, displacement, tex- 
ture gradient, etc. One can even determine the order of dependence of these 
cues and the number of cues that can be taken account of in a given time. 
The results, it is hoped, will resemble the sequential steps diagrammed in the 
flow chart of a computer program. If so, one can formalize the laws which 
relate input to output at each stage. 

Such work requires no talk of "unconscious rules" organizing fragmen- 
tary elements into perceptions. It should never lead us to say that "we have 
no immediate access to the world nor to any of its properties." What would 
be psychologically real in such a theory would not be fragments and rules, 
but just those cues in our normal perception of objects which play a role in 
the theory. 

Although we are most often not explicitly aware of them, these cues are 
not unconscious. We can become explicitly aware of them by focusing our 
attention on them, whereas we cannot become aware of neural events or even 
the "snapshots" of objects Neisser tells us we actually perceive. Sometimes 
the cues may be so slight that we would never discover them by simply 
looking. For example, one cannot see the slight displacement of each dot of 
a Julesz pattern which produces the illusion of depth. But if told what to look 
for we could presumably find the displacement with a suitable measuring 
device. Thus these cues can be said to be psychologically real in the straight- 
forward sense that we can become aware of them. 

The "flow chart" too has psychological reality in those restricted cases in 
which it expresses the order of dependence of the cues. It is surely in some 
rough way correlated with the physical processes going on in the brain, but 
even in these cases this does not justify talking of unconscious processing as 
if the brain were a digital computer operating according to a program. 

Interestingly enough, when psychologists actually undertake this sort of 
research, they find that no individual cues are necessary and sufficient but 
that different collections of cues are sufficient under specific restricted condi- 
tions. Also the order of dependence of the cues varies from situation to 
situation. The results, then, resemble a flow chart in only a very limited way 
in very sharply restricted cases. To fully formalize their theory in terms of 
their computer model the experimenters would either have to specify the 
input in terms of abstract situation-independent variables, or find metarules 
for recognizing specific situations and correlating these situations with spe- 



Notes / 236 

cific orders of dependence. So far no such abstract variables and rules have 
been found. (See my article, "Phenomenology and Mechanism," NOUS, 
Vol. V, No. 1, Feb., 1971.) 

36. Neisser, op. cit., p. 8. 

37. Ibid., p. 10. 

38. Ibid., p. 140. 

39. Fodor, Psychological Explanation, p. 26, cited in note 23 above. 

40. Ibid., p. 29. 

41. Ibid., p. 26. 

42. Ibid., p. 28. 

43. Ibid., p. 28. 

44. Ibid., p. 140. (My italics.) 

45. Ibid., p. 141. 

46. Ibid., p. 83. 

47. Ibid., p. 85. (My italics.) 

48. Ibid., p. 146. 

CHAPTER 5. THE EPISTEMOLOGICAL ASSUMPTION 

1. By "reproduction" I mean the production of essential features of the behav- 
ior in question. I do not mean an exact copy, any more than a photographic 
reproduction of the Eiffel Tower is made of steel. Since computers are not 
expected to move and exhibit behavior in the normal sense, we are not 
concerned with using the formal theory of a kind of performance to exactly 
copy that performance. The production of essential characteristics of a 
certain performance without imitating the performance in detail would 
normally be called "simulation." Thus a computer can simulate an elec- 
tion without casting any votes but the term "simulation" is already pre- 
empted by the cognitive simulationists who wish to include in their model 
not just the critical behavior but the steps by which that behavior was 
produced. 

2. This bicycle example is taken from Michael Polanyi's Personal Knowledge 
(London: Routledge & Kegan Paul), p. 49. Polanyi's analysis of the example 
is worth quoting at length: 

"From my interrogations of physicists, engineers, and bicycle manufactur- 
ers, I have come to the conclusion that the principle by which the cyclist 
keeps his balance is not generally known. The rule observed by the cyclist 
is this. When he starts falling to the right he turns the handlebars to the right, 
so that the course of the bicycle is deflected along a curve towards the right. 
This results in a centrifugal force pushing the cyclist to the left and offsets 
the gravitational force dragging him down to the right. This maneuver 
presently throws the cyclist out of balance to the left, which he counteracts 
by turning the handlebars to the left; and so he continues to keep himself 
in balance by winding along a series of appropriate curvatures. A simple 
analysis shows that for a given angle of unbalance the curvature of each 



Notes 7237 

winding is inversely proportional to the square of the speed at which the 
cyclist is proceeding. 

"But does this tell us exactly how to ride a bicycle? No. You obviously 
cannot adjust the curvature of bicycle's path in proportion to the ratio of 
your unbalance over the square of your speed; and if you could you would 
fall off the machine, for there are a number of other factors to be taken 
account in practice which are left out of in the formulation of this rule." 

In spite of this important insight that the formalism cannot account for the 
performance Polanyi blurs the significance of this example by referring to 
"hidden rules" (p. 53). This reference to hidden rules shows that Polanyi, 
like Plato, fails to distinguish between performance and competence, be- 
tween explanation and understanding, between the rule one is following and 
the rule which can be used to describe what is happening. It is just such a 
confusion which gives rise to the optimism of those in Cognitive Simulation. 
Polanyi does have an objection of his own to CS. .He holds that "in an 
important sense" we do know the rules, but claims that "one cannot deal 
with this as if it were unconscious knowledge, for the point is that it is a 
(more or less unconscious) knowledge with a bearing on an end. It is this 
quality of the subsidiary awareness, its functionally performing quality, that 
the machine cannot duplicate, because the machine operates throughout on 
one single level of awareness." (Personal communication.) This is an inter- 
esting intermediate position, but one still wonders why, granted this second 
kind of awareness, Polanyi feels it necessary to assume that we are following 
rules in any sense at all. 

3. Minsky, Computation: Finite and Infinite Machines (Englewood Cliffs, N.J.: 
Prentice-Hall, 1967), p. vii. 

4. A. M. Turing, "Computing Machinery and Intelligence," in Minds and 
Machines, ed. Alan Ross Anderson (Englewood Cliffs, N.J.: Prentice-Hall, 
1964), p. 8. 

5. Minsky, Computation: Finite and Infinite Machines, p. 107. 

6. Ibid. 

1. Turning, op. cit., pp. 22-23. 

8. Ibid. 

9. James T. Culbertson, "Some Uneconomical Robots," Automata Studies, 
C. E. Shannon and J. McCarthy, eds. (Princeton, N.J.: Princeton University 
Press, 1956), p. 100. 

10. Ibid., p. 114. 

1 1 . Why no such isolable inputs and outputs can be found will only become clear 
when we have described the relation of the human subject to his world. See 
Chapter 9, especially p. 178. 

12. Minsky, "Matter, Mind, and Models," in Semantic Information Processing, 
p. 429. 

13. H. J. Bremermann, "Optimization Through Evolution and Recombination," 
in Self-Organizing Systems (Washington, D.C, 1962), p. 1. 



Notes / 238 

14. Ibid., p. 2. 

15. Minsky, Computation, p. 107. 

16. John McCarthy, "Programs with Common Sense," in Semantic Information 
Processing, p. 410. 

1 7. Chomsky sometimes defines competence and performance so as to preserve 
this separation and to make the relation of a theory of competence to a 
theory of performance an empirical question. For example: "To avoid what 
has been a continuing misunderstanding, it is perhaps worthwhile to reiter- 
ate that a generative grammar is not a model for a speaker or a hearer. It 
attempts to characterize in the most neutral possible terms the knowledge of 
the language that provides the basis for actual use of a language by a 
speaker-hearer. When we speak of a grammar as generating a sentence with 
a certain structural description, we mean simply that the grammar assigns 
this structural description to the sentence." (Aspects of the Theory of Syntax 
[Cambridge, Mass.: M.I.T. Press, 1965], p. 9.) (My italics.) 

This straightforward definition, however, leaves some doubt as to how 
Chomsky understands the competence/performance distinction he has in- 
troduced. If competence is what one knows when one knows a language, it 
would be an empirical question whether the rules which describe compe- 
tence play any role at all in producing the performance. Yet at times 
Chomsky seems to hold that competence necessarily plays a role in perfor- 
mance and builds this into the very definition of performance and compe- 
tence and their relation: "By a 'generative grammar' I mean a description 
of the tacit competence of the speaker-hearer that underlies his actual perfor- 
mance in production and perception (understanding) of speech. A generative 
grammar, ideally, specifies a pairing of phonetic and semantic representa- 
tions over an infinite range; it thus constitutes a hypothesis as to how the 
speaker-hearer interprets utterances, abstracting away from many factors 
that interweave with tacit competence to determine actual performance." 
(Cartesian Linguistics [New York: Harper & Row, 1966], p. 75.) (My ital- 
ics.) 

Or see also ". . . We must abstract for separate and independent study a 
cognitive system, a system of knowledge and belief, that develops in early 
childhood and that interacts with many other factors to determine the kinds 
of behavior that we observe; to introduce a technical term, we must isolate 
and study the system of linguistic competence that underlies behavior but 
that is not realized in any direct or simple way in behavior." (Language and 
Mind [New York: Harcourt, Brace and World, 1968], p. 4.) (My italics.) 

When Chomsky speaks of "tacit competence" which "underlies . . . actual 
performance" and which "determines . . . behavior," we find the same 
tendency we found in Polanyi when he assumed that the rule he suggests for 
describing bicycle-riding competence is involved in bicycle-riding perfor- 
mance. On this reading, the role of the formalism expressing the competence 
is no longer neutral. Whatever the correct formalism is, it is necessarily 
involved in producing the performance. 



Notes / 239 

Yet if the competence/performance distinction is to have the effect of 
separating a formal theory from a psychological theory, the relation of a 
theory of competence to a theory of performance cannot be built in by 
definition; or to put it the other way around, if it belongs to the definition 
of competence to underlie performance, then competence cannot mean sim- 
ply a formal theory which "pairs phonetic and semantic representations over 
an infinite range." It would have to mean an idealized psychological theory 
of how language is produced, and the competence/performance distinction 
would only call attention to the fact that other factors such as fatigue and 
learning had been disregarded. 

At times Chomsky seems to hold this view. "We do not interpret what 
is said in our presence simply by application of the linguistic principles that 
determine the phonetic and semantic properties of an utterance. Extralin- 
guistic beliefs concerning the speaker and the situation play a fundamental 
role in determining how speech is produced, identified, and understood. 
Linguistic performance is, furthermore, governed by principles of cognitive 
structure (for example, by memory restrictions) that are not, properly speak- 
ing, aspects of language. 

"To study a language, then, we must attempt to disassociate a variety of 
factors that interact with underlying competence to determine actual perfor- 
mance; the technical term 'competence 1 refers to the ability of the idealized 
speaker-hearer to associate sounds and meanings strictly in accordance with 
the rules of his language." ("The Formal Nature of Language," appendix to 
Biological Foundations of Language, Eric Lenneberg, [New York: Wiley, 
1967], p. 398.) (My italics.) 

What, then, is the relation between competence and performance? If one 
discovered in psycholinguistics that language is produced in a way which 
does not involve the rules postulated by Chomsky's linguistic formalism at 
all, as the latest research seems to suggest (See T. G. Bever, The Cognitive 
Basis for Linguistic Structures, chapter entitled "The Non-Distinction Be- 
tween Linguistic Competence and Performance in the Adult": ". . . behav- 
ioral processes manipulate linguistically-defined internal and external 
structures but do not mirror or directly simulate the grammatical processes 
that relate those structures within a grammar. Such a conclusion invalidates 
any model for speech recognition which attempts directly to incorporate 
grammatical rules as an isolable component of the recognition processes." 
Preprint p. 101), would Chomsky give up his formal description? Chomsky 
seems to want to have it both ways: to make the role of his formalism for 
competence independent of psychology so he would not have to give it up 
no matter what experiments showed and yet to make its role in performance 
a matter of definition. On the one hand, he says: "When we say that a 
sentence has a certain derivation with respect to a particular generative 
grammar, we say nothing about how the speaker or hearer might proceed, in 
some practical or efficient way, to construct such a derivation. These ques- 
tions belong to the theory of language use the theory of performance." 



Notes / 240 

(Aspects of the Theory of Syntax, p. 9. [My italics.]) Yet in Language and 
Mind Chomsky says: "The problem of determining the character of such 
grammars and the principles that govern them is a typical problem of 
science, perhaps very difficult, but in principle admitting of definite answers 
that are right or wrong as they do or do not correspond to the mental reality" 
(p. 16). (My italics.) 

Underlying this uncertainty as to the status of the formal grammatical 
structure characterizing the speaker's intuitions concerning grammaticality 
is the powerful conjunction of the Platonic assumption that the formalism 
which enables us to understand behavior is also involved in producing that 
behavior, and the Kantian assumption that all orderly behavior is governed 
by rules, both reinforced by the idea of a computer program. Chomsky does 
not question the assumption that "the person who has acquired knowledge 
of a language has internalized a system of rules . . ." (Language and Mind, 
p. 23), nor that these rules function as a "mechanism" for "generating" 
sentences. These convictions taken together lead to Chomsky's Cartesian 
theory of innate ideas, which even he admits is difficult to accept: "It is not 
easy to accept the view that a child is capable of constructing an extremely 
complex mechanism for generating a set of sentences, some of which he has 
heard, or that an adult can instantaneously determine whether (and if so, 
how) a particular item is generated by this mechanism, which has many of 
the properties of an abstract deductive theory. Yet this appears to be a fair 
description of the performance of the speaker, listener, and hearer." ("A 
Review of B. F. Skinner's Verbal Behavior" The Structure of Language 
[Englewood Cliffs, N.J.: Prentice-Hall, 1964], p. 577.) 

This view, however implausible, seems acceptable thanks to the presence 
of the computer: ". . . there is no difficulty in principle in programming a 
computer with a schematism that sharply restricts the form of a generative 
grammar, with an evaluation procedure for grammars of the given form, 
with a technique for determining whether given data is compatible with a 
grammar of the given fomij with a fixed substructure of entities (such as 
distinctive features), rules, and principles, and so on in short, with a uni- 
versal grammar of the sort that has been proposed in recent years." (Lan- 
guage and Mind, p. 73.) 

Chomsky goes on to connect this computer model with the classical 
tradition: "For reasons that I have already mentioned, I believe that these 
proposals can be properly regarded as a further development of classical 
rationalist doctrine, as an elaboration of some of its main ideas regarding 
language and mind." (Language and Mind, p. 73.) He concludes: "By 
pursuing the kinds of research that now seem feasible and by focusing 
attention on certain problems that are now accessible to study, we may be 
able to spell out in some detail the elaborate and abstract computations that 
determine, in part, the nature of percepts and the character of the knowledge 
that we can acquire the highly specific ways of interpreting phenomena 
that are, in large measure, beyond our consciousness and control and that 



Notes / 241 

may be unique to man." (Language and Mind, pp. 84-85.) In this neo- 
Cartesianism the traditional philosophical assumption that man's unique 
attribute may be to be a highly sophisticated computer becomes fully ex- 
plicit, perhaps for the first time since Hobbes prematurely drew the same 
conclusion on the basis of Newtonean physics. 

18. Soren Kierkegaard, Concluding Unscientific Postscript (Princeton, N.J.: 
Princeton University Press, 1944), pp. 108 and 311. 

19. This attitude is forcefully and naively expressed in Sayre's introduction to 
The Modeling of Mind, Kenneth M. Sayre, and J. Crosson, eds. (South Bend, 
Ind.: Notre Dame University Press, 1962): 

"Any mental function which is such that (1) its input and output can be 
specified with precision, and (2) the transformation it performs can be ap- 
proximated by equations which express a determinate relationship between 
input and output, can for these reasons alone be simulated with some degree 
of adequacy. If, on the other hand, we do not have a clear understanding 
of either the input, the output, or the transformation, we will be unable to 
achieve an adequate simulation of that function. Our inability in such a case, 
however, is a discredit to the human mind, and not a symptom of any 
'transcendence' of mental functions" (p. 14). 

20. Ludwig Wittgenstein, The Blue and Brown Books (Oxford, Eng.: Basil 
Blackwell, 1960), p. 25. 

21. See, for example, Wittgenstein, Philosophical Investigations (Oxford, Eng.: 
Basil Blackwell, 1953), pp. 39, 40, 41, 42. 

"A rule stands like a sign-post. Does the sign-post leave no doubt open 
about the way I have to go? Does it show which direction I am to take when 
I have passed it; whether along the road on the footpath or cross-country? 
But where is it said which way I am to follow it; whether in the direction 
of its finger or (e.g.) in the opposite one? And if there were, not a single 
sign-post, but a chain of adjacent ones or of chalk marks on the ground 
is there any one way of interpreting them? So I can say, the sign-post does 
after all leave no room for doubt. Or rather: it sometimes leaves room for 
doubt and sometimes not. And now this is no longer a philosophical proposi- 
tion, but an empirical one" (pp. 39, 40). 

CHAPTER 6. THE ONTOLOGICAL ASSUMPTION 

1. Minsky, Semantic Information Processing, p. 11. 

2. Ibid. 

3. Of course, on one level the flip/flops are only a technical convenience, as is 
the binary system which they dictate. Any finite state machine whether with 
three-state elements, ten cog gears, or any other set of discrete states would 
dictate the same ontological conditions. For on a deeper level, the flip/flops 
represent the fact that a digital computer is a logic machine that its opera- 



Notes / 242 

tions can be represented by a truth-table, i.e., that all its information can be 
considered to be a set of propositions to which are attached the predicates 
"true" or "false," "0" or "1." 

4. Allen Newell, Learning, Generality and Problem-Solving, The RAND Cor- 
poration, RM-3285-1-PR (February 1963), p. 17. 

5. Murray Eden, "Other Pattern Recognition Problems and Some Generaliza- 
tions," in Recognizing Patterns: Studies in Living and Automatic Systems, ed. 
Kolers and Eden (Cambridge, Mass.: M.LT. Press, 1968), p. 153. (My 
italics.) 

6. Minsky, Semantic Information Processing, p. 25. 

7. Ibid., pp. 25, 26. 

8. Ibid., pp. 26, 27. 

9. Ibid., p. 27. 

10. Not that we know what it means to make our situation completely explicit 
and cannot do it. We only know what it means to make a situation suffi- 
ciently explicit for a specific purpose. 

11. See note 17. 

12. Leibniz, Selections, ed. Philip Wiener (New York: Scribner, 1951), p. 20. 

13. Ibid., p. 10. 

14. Merleau-Ponty, Phenomenology of Perception (London: Routledge & Kegan 
Paul, 1962), pp. 5, 58 ff. 

15. Martin Heidegger, DerSatz vom Grund (Pfullingen: Giinther Neske, 1957), 
p. 42. 

16. Heidegger, p. 203. In DerSatz vom Grund, Heidegger remarks: ". . . the 
determination of language as information originally supplies the basis for the 
construction of thinking machines, and for the construction of large-scale 
computer installations," and "information theory is, as pronouncement, 
already the arrangement whereby all objects are put in such form as to assure 
man's domination over the entire earth and even the planets." (My transla- 
tion.) 

17. Wittgenstein, Philosophical Investigations, p. 21. "What lies behind the idea 
that names really signify simples? Socrates says in the Theaetetus: 'If I 
make no mistake, I have heard some people say this: there is no definition 
of the primary elements so to speak out of which we and everything else 

are composed But just as what consists of these primary elements is itself 

complex, so the names of the elements become descriptive language by being 
compounded together.* Both Russell's 'individuals' and my 'objects' (Trac- 
tatus Logico-Philosophicus) were such primary elements. But what are the 
simple constituent parts of which reality is composed? ... It makes no sense 
at all to speak absolutely of the 'simple parts of a chair.' " 

18. John McCarthy, "Programs with Common Sense," in Semantic Processing 
Information, p. 403. 

19. Ibid., p. 410. 

20. Ibid., p. 411. 

21. Ibid., p. 413. 



Notes / 243 

22. Ibid., p. 411. 

23. Bar-Hillel, "The Present Status of Automatic Translation of Language," in 
Advances in Computers, ed. F. L. Alt (New York: Academic Press, 1964), 
Vol. 1, p. 94. 

24. Ibid., pp. 158, 159. It might seem from Bar-Hillers example that the com- 
puter need only check the immediate verbal context for words such as "toy" 
to determine that "playpen'* is the relevant reading. But, as John Haugeland 
has suggested, a little modification of the example will show that contextual 
analysis cannot get around Bar-HillePs objection: "Little Johnny was play- 
ing on the floor beside his pen. He was drawing a picture with a red pen on 
some green paper. When the drawing was finished, he looked for his toy box, 
and found it inside the pen/* It is conceivable that the first two occurrences 
of "pen" could be disambiguated with information from the surrounding 
words. But it is clear that since the clues to both meanings of pen are in the 
immediate verbal context of the last sentence (indeed, in the sentence it- 
self), the disambiguation of the last occurrence of "pen" requires the "com- 
mon knowledge" that Bar-Hillel has in mind. 

25. Ibid., p. 160. 

26. Minsky, Semantic Information Processing, p. 23. It might also seem that 
Bar-HillePs argument rests on accidental ambiguities which might be elimi- 
nated by subscripting the various meanings of "pen." John Haugeland, 
however, has advanced an interesting argument to show that such am- 
biguity, at least in translating between natural languages, is inevitable: 

"Imagine constructing a language Eng* which is like English except that 
the different senses of words are separated by subscripts (i.e., pen t = writing 
instrument, pen 2 = baby's enclosure, etc.). Even though this would disam- 
biguate the Bar-Hillel example, it is clear that it is really not going to be any 
easier to translate Eng* into an arbitrary target language (or into Tar*). 
Translating brother, sister, and cousin into Polynesian languages is a good 
example: In the following two tables, the columns specify the possible per- 
mutations of boy and girl for a pair of children, the rows specify geneological 
connections between them, and the boxes name their relationship under the 
various conditions. Thus, in English, a is the brother of b just in case a is 
a boy and a and b have the same parents.The problem is that brother^ which 
has only one meaning in Eng*, is ambiguous to a Tongan because it has the 
two distinct senses 'brother of a boy', and 'brother of a girl'. 

"There are two things to be seen from this example. First, ambiguity in 
the meanings of words is a relative concept. Thus, the word 'brother' is 
unambiguous relative to some languages (e.g., German), ambiguous in the 
above way to other languages, and probably ambiguous in still different ways 
relative to other languages again. Second, it would be impossible to have any 
language (say Lang*) which is unambiguous relative to all possible natural 



Notes 



/ 244 



Sex of a 
Sex of b 

a and b have the 
same parents 

a and b do not have 
the same parents, 
but the same mater- 
na! or paternal 
grandparents 



boy boy girl girl 
girl boy girl boy 



Tongan (Polynesian) 

boy boy girl girl 
girl boy girl boy 




cousin 



tu'anga'anq 




tokoua 




tu'afefine 



languages. For if any noun of Lang* is not a proper noun then it must refer 
to at least two distinguishable states of the universe. But then it is possible 
that there exists a natural language in which two common nouns are distin- 
guished by the same criterion which separates two of the referents of the one 
common noun of Lang*. Since this contradicts the hypothesis, it follows that 
Lang* can have only proper nouns (one for each distinguishable state of the 
universe), which I take to be a reduction to absurdity." (Private communica- 
tion.) 

27. Jerrold Katz and Jerry Fodor, "The Structure of a Semantic Theory," in The 
Structure of Language, Jerrold Katz and Jerry Fodor (Englewood Cliffs, 
N.J.: Prentice-Hall, 1964), p. 487. 

28. Ibid., p. 489. 

29. Ibid., pp. 489-490. 

30. Joseph Weizenbaum, "Contextual Understanding by Computers," in Recog- 
nizing Patterns, p. 181, cited in note 5 above. 

31. Ibid., p. 181. 

32. Ibid., p. 189. 

33. Ibid., pp. 181-182. 

34. Ibid., p. 182. 

35. Wittgenstein, Philosophical Investigations, p. 226. 

36. The only exception seems to be Thomas L. Jones* MAC memo no. 195, "A 
Computer Model of Simple Forms of Learning." Jones describes his pro- 
gram as follows: 

"INSIML is a computer program, written in LISP, which models simple 
forms of learning analogous to the learning of a human infant during the first 
few weeks of his life, such as learning to suck the thumb and learning to 
perform elementary hand-eye coordination. 

"The program operates by discovering cause-effect relationships and ar- 
ranging them in a goal tree. For example, if A causes B, and the program 



Notes / 245 

wants B, it will set up A as a subgoal, working backward along the chain 
of causation until it reaches a subgoal which can be reached directly; i.e. a 
muscle pull." 

This work is too rudimentary to evaluate at this stage. If this isolated thesis 
project represents a trend, it will mean a complete shift in the goal and 
methods of artificial intelligence research. 

37. Except for the alternative of facts with context-free fixed significance which 
we have seen AT workers such as Weizenbaum implicitly reject. 

Part III. Alternatives to the Traditional Assumptions 

INTRODUCTION 
1. Chomsky, Language and Mind, pp. 18-19. 

CHAPTER 7. THE ROLE OF THE BODY IN INTELLIGENT BEHAVIOR 

1 . Descartes, Discourses, Library of Liberal Arts, p. 36. 

2. Herbert Simon, The Shape of Automation for Men and Management (New 
York: Harper & Row, 1965), p. 96. 

3. Ibid., p. 40. 

4. Anthony Oettinger, "Language and Information," American Documenta- 
tion, Vol. 19, No. 3 (July 1968), p. 297. 

5. Oettinger, "The Semantic Wall," to be published by Bell Laboratories. 

6. Oettinger, "Language and Information," p. 298, cited in note 4 above. 

7. Neisser, Cognitive Psychology, p. 90. 

8. Maurice Merleau-Ponty, Phenomonology of Perception (London: Routledge 
& Kegan Paul, 1962), p. 4. 

9. Ibid., p. 68. 

10. This phenomenon lies at the basis of HusserFs whole theory of perception. 
For Husserl argued that, in recognizing an object, we give a somewhat 
determinate global meaning a noema to an otherwise indeterminate but 
determinable sensuous matter. We then proceed to make this open global 
meaning more determinate. (See E. Husserl, Ideas [New York: Collier, 
1931], Part Three. Also, my forthcoming book, HusserVs Phenomenology of 
Perception, Northwestern University Press.) 

11. John McCarthy, "Information," Scientific American, Vol. 215, No. 3 (Sep- 
tember 1966), p. 65. 

12. Neisser, op. cit., p. 86. 

13. Minsky, "Artificial Intelligence," Scientific American, Vol. 215, No. 3 (Sep- 
tember 1966), p. 257. 

14. Neisser, op. cit, p. 235. 

15. Ibid., p. 244. 

16. Ibid., p. 245. 

17. Ibid., p. 235. (My italics.) 



Notes / 246 

18. Ibid., p. 246. 

19. Of course, these field effects, like any other physical phenomenon, can be 
simulated by solving the differential equations describing the forces involved, 
but this in no way affects the Gestaltists' point which is that one could only 
simulate human behavior indirectly by simulating the physical analogue (the 
brain) not directly by programming a digital computer. 

20. Neisser, op. cit., p. 247. 

21. Donald MacKay, "A Mind's Eye View of the Brain," in Progress In Brain 
Research, 17: Cybernetics of the Nervous System (a memorial volume honor- 
ing Norbert Wiener) (Amsterdam, Holland: Elsevier Publishing Company, 
1965), p. 16. 

22. J. Piaget, Psychology of Intelligence (New York: Humanities Press, 1966), 
p. 82. 

23. Merleau-Ponty, Phenomenology of Perception, p. 142. 

24. Ibid,, p. 234. 

25. Ibid., p. 235. 

26. Ibid., p. 318. 

27. Edward Feigenbaum, "Artificial Intelligence: Themes in the Second 
Decade,'* IFIP Congress '68, Supplement, p. J-13. 

28. Michael Polanyi, Personal Knowledge: Towards a Post-Critical Philosophy 
(New York: Harper & Row, 1964), p. 59. 

29. Merleau-Ponty, op. cit, p. 106. 

30. Piaget, op. cit., p. 35. These motor schemata must have their muscular and 
neural basis, but there is no reason to suppose that these physical correlates 
go through a rule-governed sequence of independent operations. Both the 
global and underdetermined character of the motor schemata argue against 
this possibility. 

31. Wittgenstein, The Blue and Brown Books, p. 25. 

32. Michael Polanyi, "The Logic of Tacit Inference," in Knowing and Being 
(Chicago: University of Chicago Press, 1969), p. 148. Polanyi again defeats 
his own point by adding in a later account of language learning: "To the 
question, how a child can learn to perform [according to] a vast set of 
complex rules, intelligible only to a handful of experts, we can reply that the 
striving imagination has the power to implement its aim by the subsidiary 
practice of ingenious rules of which the subject remains focally ignorant." 
("Sense-giving and Sense Reading," Knowing and Being, p. 200.) 

33. Oettinger, "The Semantic Wall," to be published by Bell Laboratories. 

CHAPTER 8. THE SITUATION: ORDERLY BEHAVIOR WITHOUT RE- 
COURSE TO RULES 

1. Minsky, "Matter, Mind, and Models," Semantic Information Processing, 
p. 431. 

2. Real creativity is too much to ask. Minsky has pointed out that a minimum 
condition for problem solving is that the computer have a criterion for an 
acceptable solution: "To begin with we have to replace our intuitive require- 



Notes / 247 

ments by reasonably well-defined technical questions A minimal require- 
ment is that one have a method of discerning a satisfactory solution should 
one appear. If we cannot do this then the problem must be replaced by one 
which is well defined in that sense, and we must hope that solution of the 
substitute problem will turn out to be useful." ("Some Methods of Artificial 
Intelligence and Heuristic Programming," in Proc. Symposium on the 
Mechanization of Intelligence [London: HMSO], p. 7.) In creative work, 
however, as Newell, Shaw, and Simon note, part of the agent's task is to 
define the problem and what would count as a solution. (Newell, Shaw, and 
Simon, The Processes of Creative Thinking, The RAND Corporation, P- 
1320 [September 16, 1958], p. 4.) An artist, for example, does not have a 
criterion of what counts as a solution to his artistic problem. He invents the 
problem and the solution as he goes along. His work may later determine 
standards of success, but his success is prior to the canons later introduced 
by the critics. If the program is to be creative, the task of defining the 
problem and the rules for recognizing a satisfactory solution cannot be taken 
over by the programmer. But it is impossible to imagine how a computer 
program, which needs a definite criterion for what it's problem is and when 
it has been solved, could creatively solve a problem, or know that it had done 
so. 

3. This and the following quotations elaborating my racing example are ex- 
cerpts from a letter from Charles Taylor, growing out of a seminar he gave 
on this book while it was still in manuscript. 

4. Minsky, Semantic Information Processing, p. 27. 

5. Taylor. 

6. Martin Heidegger, Being and Time (New York: Harper & Row, 1962), 
p. 109. 

7. Wittgenstein, Philosophical Investigations, p. 50. 

8. Samuel Todes, The Human Body as the Material Subject of the World. 
Harvard doctoral dissertation, 1963. See also "Comparative Phenomenology 
of Perception and Imagination, Part I: Perception," Journal of Existential- 
ism (Spring 1966). Todes' thesis also contains interesting suggestions as to 
some very pervasive features of our experience and their function. He de- 
scribes certain characteristics of the body, e.g., that it moves forward more 
easily than backward, that it generates a right/left field, that it must balance 
itself in a gravitational field in which it can be upside down; and he shows 
the role these experiences play in our knowledge of objects. 

9. Taylor. 

10. Feigenbaum, An Information Processing Theory of Verbal Learning, P-1817 
(Santa Monica: The RAND Corporation, 1959). "In the EPAM models 
such a classification structure, termed a 'discrimination net* is the primary 
information structure. It is the product of discrimination learning, and it 
embodies at any moment all the discrimination learning that has taken place 
up to a given time. EPAM's discrimination net is a tree structure, since only 
one branch leads to any given node from above: only one pathway leads from 



Notes / 248 

the top node, or root, to any other node in the net. A stimulus object 
presented to EPAM may be classified by being sorted to a terminal node in 
the net." (William Wynn, An Information-Processing Model of Certain As- 
pects of Paired-Associate Learning, p. 5.) 

11. Wynn, op. cit., p. 9. 

12. Minsky, Semantic Information Processing, pp. 425-426. 

13. Ibid,, p. 426. 

14. Ibid., p. 427. 

15. Ibid., p. 428. 

16. Neisser, Cognitive Psychology, p. 3. 

17. Ibid. 

18. Ibid. 

19. Oettinger, "Language and Information," p. 296. 

20. Ibid. 

21. Ludwig Wittgenstein, Zettel (Berkeley: University of California Press, 
1967), p. 78. 

CHAPTER 9. THE SITUATION AS A FUNCTION OF HUMAN NEEDS 

1. Satosi Watanabe, "La Simulation mutuelle de rhomme et la machine/ 7 
Compte Rendu du Symposium sur La Technologie et Vhumanite (Lausanne, 
1965), p. 6. (My translation.) 

2. Watanabe, "Comments on Key Issues," in Dimensions of Mind, ed. Sidney 
Hook (New York: Collier, 1961), p 135. 

3. Watanabe, "Mathematical Explication of Classification of Objects," in In- 
formation and Prediction in Science, S. Dockx and P. Bernays, eds. (New 
York: Academic Press, 1965), p. 39. 

4. Heidegger, Being and Time, pp. 136-137. 

5. Friedrich Nietzsche, The Will to Power, ed. Walter Kaufman (New York: 
Vintage), p. 505. For Sartre all specific values are the product of a com- 
pletely arbitrary choice, although the demand for values is grounded in the 
human condition understood as a lack. (See Being and Nothingness, Part II, 
ch. 1, "The For-Itself and the Being of Value.") 

6. See Heidegger's analysis of being-unto-death in Being and Time, Section 
48. 

7. Herbert Simon, "Motivation and Emotional Controls of Cognition," Psycho- 
logical Review, Vol. 74, No. 1 (1967), pp. 29-39. See also, Walter R. Reit- 
man, Cognition and Thought, (New York: Wiley, 1965). 

8. N. S. Sutherland, Science Journal (October 1968), p. 48. 

9. See Samuel Todes, The Human Body as the Material Subject of the World, 
doctoral dissertation, Harvard University, 1963. The only philosopher who 
has begun to notice the difficulty the biological basis of human behavior 
poses for artificial intelligence is Keith Gunderson, who, in a recent paper 
"Philosophy and Computer Simulation" notes: "At this point the area of 



Notes / 249 

research known as biosimulation takes on far greater importance than CS." 
(Ryle, A Collection of Critical Essays, Oscar P. Wood and George Pitcher, 
eds. [New York: Anchor, 1970] p. 339.) 

10. See Soren Kierkegaard, Fear and Trembling (Garden City, N.Y.: Anchor) 
pp. 52-55. Also Concluding Unscientific Postscript. See also my forthcoming 
book on Kierkegaard to be published by Quadrangle Books. 

11. Thomas Kuhn, The Structure of Scientific Revolutions (Chicago: University 
of Chicago Press, 1962), p. 110. 

12. Ibid., p. 134. 

13. Ibid., p. 145. 

14. Ibid., p. 120. 

15. Ibid., p. 125. 

16. Ibid., p. 44. 

17. Ibid., p. 46. 

18. Ibid., p. 15. 

19. Ibid., p. 149. 

CONCLUSION 

1. Facts like "A man has two hands," rather than like "Z flip/flop is on." The 
difference is the same as the difference between a fact about the content of 
a picture and a fact about one of the dots composing the picture. It is clearly 
these real-world facts which are at stake, since Minsky suggests we have to 
deal with millions of them, not millions of bits. 

2. Minsky, Semantic Information Processing, p. 18. 

Conclusion: The Scope and Limits of Artificial Reason 

1 . It is difficult to classify and evaluate the various one-purpose programs that 
have been developed for motor design, line balancing, integrating, and so 
forth. These programs are relevant to work in artificial intelligence, but they 
are not clearly successful programs, until (a) like the chess and checker 
programs they are tested against human professionals; and (b) the problems 
attacked by these programs have, if possible, been formalized so that these 
heuristic programs can be compared with nonheuristic programs designed 
for the same purpose. (Wherever such a comparison has been made in 
checkers, logic, pattern recognition, chess the nonheuristic programs have 
proved either equal or superior to their heuristic counterparts.) 

On the other hand, programs which simulate investment banking proce- 
dures and the like have no bearing on Cognitive Simulation or Artificial 
Intelligence at all. They merely show that certain forms of human activity 
are sufficiently simple and stereotyped to be formalized. Intelligence was 
surely involved in formulating the rules which investors now follow in 
making up a portfolio of stocks, but the formalization of these rules only 
reveals them to be explicable and unambiguous, and casts no light on the 
intelligence involved in discovering them or in their judicious application. 



Notes / 250 

The challenge for artificial intelligence does not lie in such ex post facto 
formalizations of specific tasks, but rather in Area II where the formalization 
is sufficiently complex to require elegant techniques in order to reach a 
solution, in Area III where the formal system is so complex that no decision 
procedure exists and one has to resort to heuristics, and in Area IV in which 
behavior is flexible and not strictly formalizable. 

2. The activities found in Area IV can be thought of as the sort of "milestones" 
asked for by Paul Armer in his article, "Attitudes toward Intelligent Ma- 
chines": "A clearly defined task is required which is, at present, in the 
exclusive domain of humans (and therefore incontestably 'thinking') but 
which may eventually yield to accomplishment by machines," in Feigen- 
baum and Feldman, eds., Computers and Thought, p. 397. 

3. Allen Newell, J. C. Shaw, and H. A. Simon, "Empirical Explorations with 
the Logic Theory Machine: A Case Study in Heuristics," in Computers and 
Thought, p. 109. 

4. "The . . . obstacle to the expansion of a semantic information retrieval system 
is the same one which occurs in programs for theorem proving, game play- 
ing, and other areas of artificial intelligence: the problem of searching 
through an exponentially growing space of possible solutions. Here there is 
no basic transformation that can be made to avoid the mathematical fact that 
the number of possible interconnections between elements is an exponential 
function of the number of elements involved.'* (Raphael, "SIR: Semantic 
Information Retrieval," in Semantic Information Processing, p. 114.) 

5. Lecture at the University of California at Berkeley, March 1970. 

6. Neisser, Cognitive Psychology, p. 89. 

7. Ibid. (My italics.) 

8. Edward Feigenbaum, "Artificial Intelligence: Themes in the Second 
Decade," IFIP Congress *68, Final Supplement, p. J-19. 

9. Ibid. 

10. C. A. Rosen, "Machines That Act Intelligently," Science Journal, (October 
1968), p. 114. 

11. Feigenbaum, op. cit., p. J-13. 

12. Ibid. 

13. Hao Wang, "Toward Mechanical Mathematics," in The Modeling of Mind, 
Kenneth M. Sayre and Frederick J. Crosson, eds. (South Bend, Ind.: Notre 
Dame University Press, 1963), p. 93. 

14. Walter Rosenblith, Computers and the World of the Future, p. 309. 

15. J. R. Guard, F. C. Oglesby, J. H. Bennett, and L. G. Settle, "Semi- 
Automated Mathematics," Journal of the Association for Computing Ma- 
chinery, Vol. 16, No. 1 (January 1969), p. 49. 

16. Ibid., p. 57. 

17. Feigenbaum and Feldman, Computers and Thought, p. 8. 

18. Paul Armer, "Attitudes Toward Intelligent Machines," in Computers and 
Thought, p. 392. 

19. Shannon, op. cit., pp. 309-310. 



Notes / 257 

20. Enthusiasts might find it sobering to imagine a fifteenth-century version of 
Feigenbaum and Feldman's exhortation: "In terms of the continuum of 
substances suggested by Paracelsus, the transformations we have been able 
to perform on baser metals are still at a low level. What is important is that 
we continue to strike out in the direction of the milestone, the philosopher's 
stone which can transform any element into any other. Is there any reason 
to suppose that we will never find it? None whatever. Not a single piece of 
evidence, no logical argument, no proof or theorem has ever been advanced 
which demonstrates an insurmountable hurdle along this continuum." 



Index 



Abelson, 131 

AI see artificial intelligence 
Aiken, H. H., xix 

algorithmic programs, xxiii, 204, 205 
Amarel, S., 62 
ambiguity: 
and language, 19-23, 32, 35-40, 41, 118, 

126-32, 201, 206 
reduction (tolerance), 19-23, 35-40, 111, 

113, 115, 116, 118, 128-29, 130 
analogue computer, xix, xx, 73 

digital computer compared with, xix, 
106-07, 233/z-234rc; as model of the 
brain, 71-73, 157-59, 198 
analogy solving: 

Evans' program, 49-54, 58, 155, 234 
gestalt grouping, 54, 56 
Aristotle, xvi, xxvi, 109, 115, 145 
Armer, Paul, 206, 214, 250/1 
Arnheim, Rudolph, 52-54 
artificial intelligence (Artificial Intelligence 
or AI; Cognitive Simulation or CS), 
xxv-xxvii, xxix xxx, xxxiii-xxxv, 43, 
108, 136 

first-step fallacy, 41, 48, 58, 59 
future of, 203-17 

limits and problems of 12-40, 61-63, 67, 
111, 139, 197-203; storage and accessi- 
bility of large data base, 58, 59, 121, 
129, 170, 171-72, 184, 193, 194, 211, 
212 



see also Cognitive Simulation (CS); game 
playing; information processing, ho?" 
man, digital computer compared with; 
information processing, semantic; lan- 
guage translation; pattern recognition; 
problem solving 

Ashby, W. Ross, xxx 

Augustine, Saint, 22 

Babbage, Charles, xviii-xix 
Bar-Hillel, Yehoshua, 41, 171, 213 
on fallacious thinking in AI, 59 
on language translation, 3-4, 19, 20, 

126-30 passim 
behavior, human: 

body's role in, 146-67, 193 

Plato's theory, 88-89, 104, 1 14, 144, 147, 

237/z, 240/1 
see also context; information processing, 

human; situation 
Being-in-the world, 168, 173, 174, 186, 

215 

see also context; situation 
Bernstein, Alex, xxiii 
Bever, T. G., 239 n 

biological assumption (comparing man 
with computer), 68, 71-74, 118, 137 
Bobrow, Daniel: 

STUDENT Program, 44-49, 54, 55, 58, 
103 



Index 



/ 254 



body: 

role of, in intelligent behavior, 146-67, 

193 
skills acquired by, 160-61, 162, 164- 

65, 167 

Boole, George, xviii 
Boolean algebra, xviii, 68 
brain, operation and functions of, 68, 71- 

75, 78, 80, 89, 109, 155 
analogue and digital models compared, 

71-73, 157-59, 198 
Fodor's argument, 78-81, 91, 94-98, 

128, 129, 130, 198 

see also information processing, human 
Bremermann, H. J., 108-09 

Chandrasekaran, B., 10-11 

checkers, programming for, 13, 22-23, 

31, 54, 63, 207-08 
see also game playing 
chess, 112, 171 

counting out, 13, 14, 18, 24, 85, 152 
gestalt grouping, 17, 54, 56, 152-53, 

154 

programming for, xxii-xxiii, xxvii, 
xxix, xxx-xxxiii, 6, 13-18, 29-30, 34, 
41, 61, 208 

zeroing in, 14, 18, 85, 153 
see also game playing 
Chomsky, Noam, xxvii, 110, 145, 146, 

238n, 241 n 
cognitive psychology, 90, 181, 199, 235 n 

see also intellectualists; mentalists 
Cognitive Simulation (CS), xxxiii, xxxv, 
3-41, 43, 51-52, 63, 67, 75, 76, 78- 
86, 87, 89, 101, 137, 169, 197, 198, 
200, 201, 215 

see also artificial intelligence; informa- 
tion processing, human, digital com- 
puter compared with 
competence and performance, 110-14, 

237 n, 238n-241n 
computer see analogue computer; digital 

computer 
context, 168-183 
role in language understanding, 126- 

33, 136, 153, 154, 207 

situation as field for encountering facts, 

122, 146, 172-175, 181-183, 200-202 

situation as opposed to physical state, 

126, 148; ambuiguity reduction or 



tolerance, 19-23, 35-40, 111, 113, 
115, 116, 118, 128-29, 130-136 

counting out see chess, counting out 

Crosson, Frederick, 225n-226n 

CS see Cognitive Simulation 

Culbertson, James T., 105-06 

cybernetics, xvi, xx, xxii, 42, 71, 77, 78, 139 

de Groot, A. D., xxxii, 16-17, 29-30 

Descartes, Rene, 89, 147-48, 158, 160, 178, 
180, 240/t 

digital computer, xviii-xx, 67-6*8, 73, 107, 

143 

analogue computer compared with, xix, 
106-07, 233rt-234rt; as model of the 
brain, 71-73, 157-59, 198 
human information processing compared 
with, 48^9, 63, 167-69, 192, 193; bio- 
logical assumption, 68, 71-74, 118, 
137; cybernetic theory, xx, xxii, 77, 78; 
epistemological assumption, 68, 69, 
101-17, 118, 119, 138, 139, 143, 147, 
168, 200; ontological assumption, 68, 
69, 117-36, 138, 139, 143, 199-200; 
psychological assumption, xxx, 41, 
48-49, 68, 69, 75-100, 118, 123, 137- 
38, 139, 143, 147, 177, 194, 197-202, 
223 n 

discrimination and insight (in human infor- 
mation processing), 24-34, 39, 99, 1 1 8, 
169-77, 198, 201, 206, 207, 208, 210 
see also zeroing in 

Ebbinghaus, Hugo, 22 
Eckert, J. P., xx 
Eden, Murray, 9-10, 50-51, 120 
empiricism, 68, 69, 81, 82-86, 90, 98, 99, 
123, 137, 138, 177, 197-98, 199, 201 
epistemological assumption (comparing 
man with computer), 68, 69, 101-17, 
118, 119, 138, 139, 143, 147, 155, 168, 
200 

Ernst, G. W., 8 
Evans, Thomas G.: 

analogy program, 49-54, 58, 155, 234 n 
exponential growth, problem of, 19, 205, 

207 

in game playing, 13, 14, 18 
in pattern recognition, 33, 108 



Index 



/ 255 



Feigenbaum, Edward A., 135 
on artificial intelligence, 210 
Computers and Thought, xxx, 12, 43, 

214, 224/i 

EPAM program, 21, 176 
on learning programs, 30-31 
on robot projects, 164, 211-12 
Feldman, Julian: 

Computers and Thought, xxx, 12, 43, 

214, 224/z 
Fodor, Jerry A.: 
assumption of computer model, 78-81, 

91, 94-98, 198 
semantic theory, 128, 129 
fringe consciousness, 1 1 8 
and game playing, 12-19, 207 
and language, 20, 21 
and pattern recognition, 32, 33-35, 39, 
40 

Galanter, Eugene, 28-29, 81, 86-87, 91, 95, 

97, 98, 99 

Galileo, xvi, 109, 125 
game playing (and game playing pro- 
grams), xxix, xxxiii, 9, 12-13, 18-19, 
40, 41, 82, 138, 149, 177, 198, 204, 205, 
206, 211, 214 

exponential growth, 13, 14, 18 
fringe consciousness, 12-19, 207 
gestalt grouping, 17, 54, 56, 152-53, 154 
see also checkers; chess 
Gelb, Adhemar, 35 
Gelernter, H.: 

Geometry Theorem Machine, xxx, 8, 54 
General Problem Solver (GPS), xxiv-xxv, 
xxix, 5-6, 8, 24-26, 31-32, 49-50, 52, 
61, 119-20, 208 
gestalt grouping: 
in game playing (chess), 17, 54, 56, 152- 

53, 154 

in language, 156-57, 182 
in pattern recognition and perception, 
17, 18, 21, 26, 54, 56, 77-78, 149-60 
passim. 167, 182, 198, 208-09, 226n 
Giuliano, Vincent E., 1 1 
Gold, Bernard, 9 
Goldstein, Kurt, 35 
GPS see General Problem Solver 
Greenblatt, Richard: 
MacHack chess program, xxxii-xxxiii, 
14-15, 208 



Greenwood, P. E., 63 
Gruenberger, Fred, xxxii 
Gunderson, Keith, 248/i-249/z 
Gurwitsch, Aron, 34, 39 

Haugeland, John, 243n-244/i 

Hawaii International Conference on the 
Methodologies of Pattern Recogni- 
tion, 10 

Hearst, Eliot, xxxii, 17, 29-30 

Hebb, Donald, 209 

Heidegger, Martin, xx, 124, 139, 145, 164, 
173-74, 182, 183, 186-87, 188 

heuristic programs, xxiii-xxv, xxvi, 5-6, 8, 
12-19, 24-30, 34, 43, 56, 86, 99, 114, 
205, 206 

see also Cognitive Simulation (CS); infor- 
mation processing, semantic 

Hobbes, Thomas, xvii, xx, 125, 241 n 

Hume, David, 68, 90, 123 

Hunt, Earl, 33 

Husserl, Edmund, 152, 153, 154, 160, 162, 
245/z 

"information processing,'* human, 135, 
168, 202 

ambiguity reduction or tolerance, 19-23, 
35-40, 111, 113, 115, 116, 118, 128- 
29, 130 

context see context 

digital computer compared with, 4849, 
63, 67-69, 192, 193; biological as- 
sumption, 68, 71-74, 118, 137; cyber- 
netic theory, xx, xxii, 77, 78; 
epistemological assumption, 68, 69, 
101-17, 118, 119, 138, 139, 143, 147, 
155, 168, 200; ontological assumption, 
68, 69, 117-36, 138, 139, 143, 199- 
200; psychological assumption, xxx, 
41, 48-49, 68, 69, 75-100, 118, 123, 
137-38, 139, 143, 147, 177, 194, 197- 
202, 223/1 

discrimination and insight, 24-34, 39, 99, 
118, 169-77, 198, 201, 206, 207, 208, 
210 

fringe consciousness, 12-19, 20, 21, 32, 
33-35, 39, 40, 118,207 

gestalt grouping, 17, 18, 21, 26, 54, 56, 
77-78, 149-60 passim, 167, 182, 198, 
208-09, 226n 



Index 



7256 



"information processing," (continued) 
perspicuous grouping, 32-40, 1 1 8 
situation see situation 
see also brain, operation and functions 

of 
information processing, misleading use of 

technical term, 77, 78 
information processing, semantic, xxxiii, 

42-63 
Bobrow's STUDENT Program, 44-49, 

58, 103 
Evans' analogy program, 49-54, 58, 155, 

234/1 
Minsky on, 43-49 passim, 51, 54, 57-62 

passim, 119, 120-23, 127, 129, 153, 

171-72, 194, 211 

and physics, laws of, 103-09, 155 
Quillian's Semantic Memory Program, 

54-57, 58, 234/z 
see also artificial intelligence; language 

and linguistics; language learning; lan- 
guage translation 

insight see discrimination and insight 
intellectualists, 68, 76, 88, 90, 91, 107, 123, 

145, 233 n 
see also cognitive psychology; mentalists 

James, William, 15 

Jones, Thomas L., 244-245/z 

Kanal, Laveen, 10-11 

Kant, Immanuel, 68, 88, 90, 113, 136, 158, 

160, 240n 

Katz, Jerrold, 128, 129, 130 
Kierkegaard, Soren, 113, 189, 191 
Kubrick, Stanley, xxviii 
Kuhn, Thomas, 83, 84, 189-91 
Kuno, 19 

language and linguistics, 20, 82, 103, 109- 

14, 116, 149, 150, 157-59, 187, 198, 

215 
and ambiguity, 19-23, 32, 35-40, 41, 

118, 126-32,201, 206 
context and situation, importance of 

recognizing, 126-33, 136, 153, 154, 

207 

fringe consciousness, 20, 21 
gestalt grouping, 156-57, 182 
transformational linguistics, 110, 157-58 
see also information processing, semantic 



language learning, xxii, xxix, 22, 23, 166, 

204, 205 

EPAM program, 21, 176 
nonsense syllables, 21-22 
language translation (and language trans- 
lation programs), xxiii, xxvii, xxxiii, 
3-5, 9, 40, 41, 61, 126, 138, 150-51, 

159, 168, 171, 204, 205, 208, 213, 
214 

Bar-Hillel on, 3-4, 19, 20, 21, 126-30 

passim 
Oettinger on, xxiii, 3, 4, 19-20, 150, 153, 

167, 182, 213 

see also language and linguistics 
Learning, xxii, 22, 23, 30, 31, 47, 62, 135, 

202, 244 
Leibniz, Gottfried Wilhelm von, xvii-xviii, 

xx, xxii, 113, 123, 213 
Lettvin, Jerome, 73 
Licklider, J. C. R., xxxv 
Life Magazine, xxviii 
Lincoln Laboratory, 9 
Logic Theory Machine (LT), xxiii-xxiv, 5, 

6, 63, 206 

McCarthy, John, 109, 125-26, 155, 174 
MacKay, Donald, 159 
machine trace: 
human protocol compared 1 with, 5, 24, 

29, 82-85, 212, 224n 
Massachusetts Institute of Technology: 

robot project, 163-64, 215 
Mathematics: 
purely mechanical, 8 
machine aided, 213, 214 
Mauchly, J., xx 
means-end analysis, 5, 24, 120, 184, 207, 

211 
vs. pragmatic structuring, 146, 174, 181, 

184-92 

see also General Problem Solver (GPS) 
mentalists, 68, 76, 88, 90, 91, 107, 123, 145, 

233 

see also cognitive psychology; intellectu- 
alists 
Merleau-Ponty, Maurice, 124, 145, 152, 

160, 161-62, 164-65 
Michie, Donald, xxxii 

Miller, George, 28-29, 76, 81, 86-87, 91, 
95, 97, 98, 99 



Index 



7257 



Minsky, Marvin, xvii, xxxv, 138, 179, 234 n 
on artificial intelligence, 5, 71, 135 
on behavior, 103-04, 107, 108, 109, 

168-69 

on effective procedure, xvi 
on global recognition, 155, 156, 208-09 
on heuristics, 210, 226 n 
on the history of machine intelligence, 

42-43 

on imagination, 179, 180 
on knowledge as neutral data, 190 
on machines and their potential, xxviii- 

xxix, 22-23, 58, 61, 76 
ontological assumptions, 125 
on perceptrons, 71, 234 n 
on problem solving, 6, 26-30, 174 
robot project, 163-64, 215 
on semantic information processing, 

43-49 passim, 51, 54, 57-62 passim. 

119, 120-23, 127, 129, 153, 171-72, 

194, 211 

National Academy of Sciences: 

National Research Council report, 4, 5 
Neisser, Ulric, 91 

on cognition, 76, 87, 92-94, 95, 97, 98, 

155, 156-57, 158, 159, 180-81, 182, 199 

on gestalt interpretation, 155, 156-57, 

209 

on pattern recognition, 9, 10, 33, 34, 209 
Neitzsche, Friedrich, 187 
Neumann, John von, 72, 73 
Newell, Allen, 81 
chess, programming for, xxii-xxiii, xxxi, 

13, 14, 16-17 

on creative thinking, 27-28 
and heuristic programming, xxiii-xxv, 5, 

8, 28, 29, 34, 43, 56, 99, 205 
on information processing, 67 
on problem solving, 6, 52, 247 n; human 
protocols and machine traces com- 
pared, 82-85, 212 
on synthesis of behaviorists and Gestalt- 

ists, 233 n 
theorem-proving program, xxx, xxxi, 5, 

41, 205 

see also Cognitive Simulation (CS); Gen- 
eral Problem Solver (GPS); Logic The- 
ory Machine (LT) 
Newton, Sir Isaac, 83, 84, 85, 241 n 
Nonrulelike behavior, 20, 86-90, 101, 102, 



109-115, 165, 183, 190, 191, 198-200, 
237 , 240 n 
see also rule-governed behavior 

Oettinger, Anthony G.: 

on language translation, xxiii, 3, 4, 19- 
20, 150, 153, 167, 182, 213 

Papert, Seymour, xxxii, xxxv, 21, 208-09, 

210, 234n 
Pappus, xxx 

parallel processing, 226n-221n 
Pascal, Blaise, 20, 35, 115, 192, 205, 206 
pattern recognition (and pattern recogni- 
tion programs), xxvii, xxix, xxxiii, 9- 
11, 32-40, 41, 49, 51, 82, 138, 
149-62, 171, 204, 205, 213, 214, 215 
classification by traits, 32-40 
discrimination and insight, 24-34, 39, 

118 

exponential growth, 33, 108 
fringe consciousness, 32, 33-35, 39, 40 
generic, 35-36, 39, 40, 167, 187, 206 
gestalt grouping, 17, 26, 54, 56, 149-60 
passim, 167, 182, 198, 208-09, 226n 
resemblance, 36-38, 40, 41, 167, 198 
similarity, 38-39, 40, 41, 167, 198 
typicality, 38-40 

see also context; perception; situation 
perception, 32-41 passim, 94-98, 150, 

151-54, 161 

gestalt grouping, 17, 26, 54, 56, 149-60 
passim, 167, 182, 198, 208-09, 226/2 
perceptrons, 71, 234 n 
performance and competence, 110-14, 

237n, 238n-241rt 

perspicuous grouping (in human informa- 
tion processing), 32-40, 118 
phenomenology, 89-94, 95, 96, 100, 112, 

124, 153-94, 199, 200, 245 
see also Husserl, Edmund; Merleau- 

Ponty, Maurice 
physics, laws of: 
and information processing, 103-09, 

155 

Piaget, J., 161, 165, 202 
Pierce, John, 213 

Plato (and Platonic theory), xv-xvi, xx, 
xxii, 98, 102, 115, 123, 124, 143, 145 
on human behavior, 88-89, 104, 114, 
144, 147, 237n, 240 



Index 



/ 258 



Polanyi, Michael, 15, 145, 164, 166, 236- 

237 n 

Polya, George, 28 
Pribam, Karl R, 28-29, 81, 86-87, 91, 95, 

97, 98, 99 

problem solving (and problem solving pro- 
grams), xxix, xxxiii, 5-9, 40, 41, 42-61, 

138, 168, 169, 171, 184, 191, 198, 211, 

213, 214 

counting out, 13, 14, 18, 24, 85, 152 
discrimination and insight, 24-32, 99, 

169-77, 198, 201, 206, 207, 208, 210 
means-ends analysis, 5, 24, 120, 184, 207, 

211; vs. pragmatic structuring, 146, 

174, 181, 184, 184-92 
protocol, human, and machine trace 

compared, 5, 24, 82-85, 212, 224 
trial-and-error search, 6, 24-32, 138, 

204, 205 
zeroing in, 14, 18, 85, 99, 153, 172, 174, 

184 
see also context; exponential growth; 

game playing; General Problem Solver 

(GPS); Logic Theory Machine (LT); 

situation 

programs, 129, 249/i-250 
algorithmic, xxiii, 204, 205 
parallel and serial processing, 226 n- 

221 n 
see also heuristic programs; individual 

programs 

protocol, human (in problem solving), 224 n 
machine trace compared with, 5, 24, 29, 

82-85, 212, 224w 
psychological assumption (use of computer 

model in psychology), xxx, 41 , 4849, 

68, 69, 75-100, 118, 137-38, 139, 143, 

147, 194, 199-200, 223n 
a priori arguments, 69, 81, 86-99, 137, 

177, 198, 199, 202 
empirical evidence, 69, 81, 82-86, 90, 98, 

99, 123, 137, 138, 177, 197-98, 199, 

201 
Pudovkin, V. I., 37 

Quillian, Ross: 

Semantic Memory Program, 5457, 58, 
234/z 

RAND Corporation, xxxiv-xxxv, 227 n 
Reitman, Walter R., 187-88 



relevance of facts see discrimination and 

insight 

robot projects, 163-64, 211-12, 215 
Rosen, C. A., 211 
Rosenblatt, Frank, 42, 150 
Rosenblith, Walter A., 74, 213 
Rubin, Edgar: 

Peter-Paul Goblet, 151-52 
Rule-governed behavior, xv, 68, 86-90, 95, 
101, 102, 104, 144, 183 

see also nonrulelike behavior 
Russell, Bertrand, 123 

Samuel, A. L., 208 

Checker Program, 22-23, 31, 54, 63, 

207-08 

Sartre, Jean-Paul, 187 
Sayre, Kenneth M., 241 
scientific theory, 83, 113 
Scriven, Michael, xxxi, 23 
Selfridge, Oliver G., 9, 10, 33, 34 
semantic information processing see 
information processing, semantic 
Semi-Automated Mathematics (SAM), 

213-14 

serial processing, 226n~227n 
Shannon, Claude E., xxii, 77, 78, 216 
Shaw, J. C, 8, 81 

chess, programming for, xxiii, xxxi, 14 

on creative thinking, 27-28 

and heuristic programming, xxiii-xxv, 5, 

8, 28, 29, 99, 205 

on problem solving, 247 n; human proto- 
cols and machine traces compared, 
82-85, 212 
on synthesis of behaviorists and Gestalt- 

ists, 233n 
theorem-proving program, xxx, xxxi, 5, 

41, 205 

see also Cognitive Simulation (CS); Gen- 
eral Problem Solver (GPS); Logic The- 
ory Machine (LT) 
Simon, Herbert A., xxxiii, xxxv, 54, 55, 81, 

138 

on artificial intelligence, 71, 135 
chess, programming for, xxiii, xxxi- 

xxxii, 13-14, 16-17 
on creative thinking, 27-28 
on emotion and motivation, 187-88 
and heuristic programming, xxiii-xxv, 



Index 



Simon, Herbert A. (continued) 

xxvi, 5, 8, 28, 29, 34, 43, 56, 76, 86, 99, 
205, 206 

on information processing, 67 
on machines and their potential, xxix- 

xxx, 41, 76, 148, 149 
on problem solving, 247 n; human proto- 
cols and machine traces compared, 
82-85, 212 
on synthesis of behaviorists and Gestalt- 

ists, xxx, 41, 76, 23 3 n 
theorem-proving program, xxx, xxxi, 5, 

41, 205 

unfulfilled predictions, 25, 26 
see also Cognitive Simulation (CS); Gen- 
eral Problem Solver (GPS); Logic The- 
ory Machine (LT) 

situation, 113, 120, 122, 125-32, 135, 136 

as basis of nonrulelike behavior, 168-183 

as field for encountering facts, 122, 126- 

35, 146, 200, 201, 202, 215; ambiguity 

reduction or tolerance, 19-23, 35-40, 

111, 113, 115, 116, 118, 128-29, 130 

as function of needs and purposes, 146, 

174, 181, 184-92, 194, 211 
as opposed to physical state, 126, 148 
Socrates, xv, 187 
Solomonoff, R. J., 61-62 
S-R psychology, 90, 91, 203 
Stanford Research Institute: 
robot project, 164, 211-12 
Stanford University: 
robot project, 164 
Sutherland, N. S., 188 

Taylor, Charles, 145, 17O-71, 172-73, 175 
theorem proving (and theorem-proving 

programs), 61, 108, 204, 208 
Gelernter, xxx, 8 
Newell, Shaw, and Simon, xxx, xxxi, 5, 

41, 205 
SAM, 213-14 
Wang, 8, 205 



Todes, Samuel, 145, 174, 189 

Toffler, Alvin, 223 n, 229-230n 

Tonge, Fred M., 62-63 

trial-and-error (in problem solving), 6, 24- 

32, 138, 204, 205 
see also chess, counting out 
Turing, A. M.: 

on formalization of behavior, 104-05, 

107, 108, 109, 115 
on machines and machine intelligence, 

xix, xx-xxi, xxii, xxviii, 21, 22, 104, 
Turing Test, xxi, xxvi 
2001: A Space Odyssey, xxviii 

Uhr, Leonard, 10, 32 
Vossler, Charles, 10, 32 

Wang, Hao, 212 

theorem-proving program, 8, 205 
Watanabe, Satosi, 185-86, 187 
Weaver, Warren, 77 
Weizenbaum, Joseph, 130-32, 133, 135, 

136 

Wertheimer, Max, 26, 28, 29 
Wiener, Norbert, xvi, xxxi 
Wittgenstein, Ludwig, 37, 104, 133, 145 

on atomic facts, 123, 124, 242 

on distinguishing between symptom and 
criterion, 36 

on facts and context, 174, 175, 182, 183 

on family resemblance, 3840 

on language and rules, 20, 22, 23, 115, 
116, 166 

ontological arguments, 123, 124 

on recognition, 38-39, 40 
Wynn, William, 176, 177, 247 

Zadeh, L. A., 228w-299n 

zeroing in (in problem solving), 99, 172, 

174, 184 
in chess, 14, 18, 85, 153 




, Cambridge 



HUBERT DREYFUS is a graduate of 
Harvard. University and has taught at 
Harvard, Brandeis, and Nf .I.X. He pur- 
sued the question of whether or not 
digital computers could. t>e programmed 
to l>ehave intelligently first as a consul- 
tant at RAND and then, with National 
Science Foundation support, as a Re- 
search Associate in Computer Sciences 
at the Harvard Computation Labora- 
tory. He is an Associate Professor of 
Philosophy at the University of Cali- 
fornia at Berkeley. 



Jacket design by Hal Sfegel